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Sample records for granger causality approach

  1. Paradoxical Behavior of Granger Causality

    Science.gov (United States)

    Witt, Annette; Battaglia, Demian; Gail, Alexander

    2013-03-01

    Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

  2. Determinants of Current Account Deficit in Turkey: The Conditional and Partial Granger Causality Approach

    OpenAIRE

    YURDAKUL, Funda; CEVHER, Erdogan

    2015-01-01

    This study aims to reveal the causality relations between the macro aggregates that affect current deficit using conditional and partial Granger causality test. Current deficit/GDP, growth rate, real effective exchange rate, direct foreign capital investment, openness, and energy import were selected as variables for this purpose. 2003.1-2014.2 quarterly data for Turkey’s economy were used for analysis. The results of the conditional and partial Granger causality test demonstrate that real ef...

  3. Granger Causality and Unit Roots

    DEFF Research Database (Denmark)

    Rodríguez-Caballero, Carlos Vladimir; Ventosa-Santaulària, Daniel

    2014-01-01

    The asymptotic behavior of the Granger-causality test under stochastic nonstationarity is studied. Our results confirm that the inference drawn from the test is not reliable when the series are integrated to the first order. In the presence of deterministic components, the test statistic diverges......, eventually rejecting the null hypothesis, even when the series are independent of each other. Moreover, controlling for these deterministic elements (in the auxiliary regressions of the test) does not preclude the possibility of drawing erroneous inferences. Granger-causality tests should not be used under...

  4. Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

    Science.gov (United States)

    Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

    2010-06-21

    Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

  5. Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

    Directory of Open Access Journals (Sweden)

    Guo Shuixia

    2010-06-01

    Full Text Available Abstract Background Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE, Bayesian networks, information theory and Granger Causality. Results Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins. For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. Conclusions The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

  6. Granger Causality Testing with Intensive Longitudinal Data.

    Science.gov (United States)

    Molenaar, Peter C M

    2018-06-01

    The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

  7. Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

    Science.gov (United States)

    Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

    2009-12-01

    Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

  8. BOLD Granger causality reflects vascular anatomy.

    Directory of Open Access Journals (Sweden)

    J Taylor Webb

    Full Text Available A number of studies have tried to exploit subtle phase differences in BOLD time series to resolve the order of sequential activation of brain regions, or more generally the ability of signal in one region to predict subsequent signal in another region. More recently, such lag-based measures have been applied to investigate directed functional connectivity, although this application has been controversial. We attempted to use large publicly available datasets (FCON 1000, ADHD 200, Human Connectome Project to determine whether consistent spatial patterns of Granger Causality are observed in typical fMRI data. For BOLD datasets from 1,240 typically developing subjects ages 7-40, we measured Granger causality between time series for every pair of 7,266 spherical ROIs covering the gray matter and 264 seed ROIs at hubs of the brain's functional network architecture. Granger causality estimates were strongly reproducible for connections in a test and replication sample (n=620 subjects for each group, as well as in data from a single subject scanned repeatedly, both during resting and passive video viewing. The same effect was even stronger in high temporal resolution fMRI data from the Human Connectome Project, and was observed independently in data collected during performance of 7 task paradigms. The spatial distribution of Granger causality reflected vascular anatomy with a progression from Granger causality sources, in Circle of Willis arterial inflow distributions, to sinks, near large venous vascular structures such as dural venous sinuses and at the periphery of the brain. Attempts to resolve BOLD phase differences with Granger causality should consider the possibility of reproducible vascular confounds, a problem that is independent of the known regional variability of the hemodynamic response.

  9. Hemispheric lateralization in top-down attention during spatial relation processing: a Granger causal model approach.

    Science.gov (United States)

    Falasca, N W; D'Ascenzo, S; Di Domenico, A; Onofrj, M; Tommasi, L; Laeng, B; Franciotti, R

    2015-04-01

    Magnetoencephalography was recorded during a matching-to-sample plus cueing paradigm, in which participants judged the occurrence of changes in either categorical (CAT) or coordinate (COO) spatial relations. Previously, parietal and frontal lobes were identified as key areas in processing spatial relations and it was shown that each hemisphere was differently involved and modulated by the scope of the attention window (e.g. a large and small cue). In this study, Granger analysis highlighted the patterns of causality among involved brain areas--the direction of information transfer ran from the frontal to the visual cortex in the right hemisphere, whereas it ran in the opposite direction in the left side. Thus, the right frontal area seems to exert top-down influence, supporting the idea that, in this task, top-down signals are selectively related to the right side. Additionally, for CAT change preceded by a small cue, the right frontal gyrus was not involved in the information transfer, indicating a selective specialization of the left hemisphere for this condition. The present findings strengthen the conclusion of the presence of a remarkable hemispheric specialization for spatial relation processing and illustrate the complex interactions between the lateralized parts of the neural network. Moreover, they illustrate how focusing attention over large or small regions of the visual field engages these lateralized networks differently, particularly in the frontal regions of each hemisphere, consistent with the theory that spatial relation judgements require a fronto-parietal network in the left hemisphere for categorical relations and on the right hemisphere for coordinate spatial processing. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  10. Granger-Causality Maps of Diffusion Processes

    Czech Academy of Sciences Publication Activity Database

    Wahl, B.; Feudel, U.; Hlinka, Jaroslav; Wächter, M.; Peinke, J.; Freund, J.A.

    2016-01-01

    Roč. 93, č. 2 16 February (2016), č. článku 022213. ISSN 2470-0045 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Granger causality * stochastic process * diffusion process * nonlinear dynamical systems Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.366, year: 2016

  11. Conditional Granger Causality of Diffusion Processes

    Czech Academy of Sciences Publication Activity Database

    Wahl, B.; Feudel, U.; Hlinka, Jaroslav; Wächter, M.; Peinke, J.; Freund, J.A.

    2017-01-01

    Roč. 90, č. 10 (2017), č. článku 197. ISSN 1434-6028 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Granger causality * stochastic process * diffusion process * nonlinear dynamical systems Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 1.461, year: 2016

  12. Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

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    Lei Jiang

    2017-12-01

    Full Text Available The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China’s GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.

  13. Characterizing time series: when Granger causality triggers complex networks

    International Nuclear Information System (INIS)

    Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

    2012-01-01

    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

  14. Characterizing time series: when Granger causality triggers complex networks

    Science.gov (United States)

    Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

    2012-08-01

    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

  15. Economic Growth, Foreign Direct Investment and CO2 Emissions in China: A Panel Granger Causality Analysis

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    Hongfeng Peng

    2016-03-01

    Full Text Available Using a sample of province-level panel data, this paper investigates the Granger causality associations among economic growth (GDP, foreign direct investment (FDI and CO2 emissions in China. By applying the bootstrap Granger panel causality approach (Kónya, 2006, we consider both cross-sectional dependence and homogeneity of different regions in China. The empirical results support that the causality direction not only works in a single direction either from GDP to FDI (in Yunnan or from FDI to GDP (in Beijing, Neimenggu, Jilin, Shanxi and Gansu, but it also works in both directions (in Henan. Moreover, we document that GDP is Granger-causing CO2 emissions in Neimenggu, Hubei, Guangxi and Gansu while there is bidirectional causality between these two variables in Shanxi. In the end, we identify the unidirectional causality from FDI to CO2 emissions in Beijing, Henan, Guizhou and Shanxi, and the bidirectional causality between FDI and CO2 emissions in Neimenggu.

  16. Financial networks based on Granger causality: A case study

    NARCIS (Netherlands)

    Papana, A.; Kyrtsou, C.; Kugiumtzis, D.; Diks, C.

    Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to

  17. New Insights into Signed Path Coefficient Granger Causality Analysis.

    Science.gov (United States)

    Zhang, Jian; Li, Chong; Jiang, Tianzi

    2016-01-01

    Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

  18. Partial Granger causality--eliminating exogenous inputs and latent variables.

    Science.gov (United States)

    Guo, Shuixia; Seth, Anil K; Kendrick, Keith M; Zhou, Cong; Feng, Jianfeng

    2008-07-15

    Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality--Granger causality--that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails.

  19. Testing for Granger causality in large mixed-frequency VARs

    NARCIS (Netherlands)

    Götz, T.B.; Hecq, A.W.

    2014-01-01

    In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels (2012), where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a

  20. Increasing fMRI sampling rate improves Granger causality estimates.

    Directory of Open Access Journals (Sweden)

    Fa-Hsuan Lin

    Full Text Available Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magnetic resonance imaging (fMRI is increased 20-fold. Specifically, healthy volunteers performed a simple visuomotor task during blood oxygenation level dependent (BOLD contrast based whole-head inverse imaging (InI. Granger causality analysis based on raw InI BOLD data sampled at 100-ms resolution detected the expected causal relations, whereas when the data were downsampled to the temporal resolution of 2 s typically used in echo-planar fMRI, the causality could not be detected. An additional control analysis, in which we SINC interpolated additional data points to the downsampled time series at 0.1-s intervals, confirmed that the improvements achieved with the real InI data were not explainable by the increased time-series length alone. We therefore conclude that the high-temporal resolution of InI improves the Granger causality connectivity analysis of the human brain.

  1. Functional clustering of time series gene expression data by Granger causality

    Science.gov (United States)

    2012-01-01

    Background A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them. PMID:23107425

  2. Energy use, emissions, economic growth and trade: A Granger non-causality evidence for Malaysia

    OpenAIRE

    Ismail, Mohd Adib; Mawar, Murni Yunus

    2012-01-01

    This paper investigates the relationship among energy, emissions and economic growth in Malaysia with the presence of trade activities. We employ Johansen’s (1995) approach to investigate the relationship. Using annual data from 1971 to 2007, the empirical results shows that there are long-run causalities among energy, emission and economic growth, and among energy, emissions, export and capital, while the short-run Granger non-causality test shows that there are unidirectional causalities ru...

  3. Granger Causality between Stock Market and Macroeconomic Indicators: Evidence from Germany

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    Tomáš Plíhal

    2016-01-01

    Full Text Available The aim of this paper is to investigate informational efficiency of the stock market in Germany. Granger causality between the stock market and the selected macroeconomic variables is investigated by bivariate analysis using Toda-Yamamoto (1995 approach. This study focuses on monthly data from January 1999 to September 2015, and the stock market is represented by blue chip stock market index DAX. Investigated macroeconomic indicators include industrial production, inflation, money supply, interest rate, trade balance and exchange rate. Stock market Granger-causes industrial production and interest rate, and is therefore leading indicator of these variables. Between money supply and stock prices is Granger causality in both directions. Other variables seem to be independent on development of the stock market. We do not find any violation of Efficient market hypothesis which indicates that the stock market in Germany is informational efficient.

  4. Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

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    Wanzeng Kong

    2015-08-01

    Full Text Available Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

  5. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    Science.gov (United States)

    Nemani, Ramakrishna R.

    2016-01-01

    Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

  6. Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

    Science.gov (United States)

    Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

    2018-04-01

    Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

  7. Financial networks based on Granger causality: A case study

    Science.gov (United States)

    Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

    2017-09-01

    Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

  8. ANALISIS KETERKAITAN KETERSEDIAAN INFRASTRUKTUR DENGAN PERTUMBUHAN EKONOMI DI INDONESIA: PENDEKATAN ANALISIS GRANGER CAUSALITY

    Directory of Open Access Journals (Sweden)

    Lesta Karolina B Sembanyang

    2015-12-01

    Full Text Available The aims of this study are to analyze the causal relationship of public service provision (infrastructure, economic growth and tax inIndonesiaand to formulate the policy implications of causal link and infrastructure inIndonesia’s economic growth. The data used was time series data, from 1987 up to 2009. They were from many sources such as Government Expenditure (APBN, Central Bureau of Statistics (BPS and the International Financial Statistics (IFS. The method used is a causal analysis approach or the Granger causality. The findings of this study is that there is a direct relationship between GDP to infrastructure and the GDP to tax revenue. The conclusions of this study are Gross Domestic Product (GDP can lead the availability of infrastructure (for example road length in Indonesia,there is a causal connection between the economic growth and the tax revenue in Indonesia, andthe increased tax revenue will increase the availability of infrastructure, especially road.

  9. Coal consumption and economic growth nexus: Evidence from bootstrap panel Granger causality test

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    Anoruo Emmanuel

    2017-01-01

    Full Text Available This paper explores the causal relationship between coal consumption and economic growth for a panel of 15 African countries using bootstrap panel Granger causality test. Specifically, this paper uses the Phillips-Perron unit root test to ascertain the order of integration for the coal consumption and economic growth series. A bootstrap panel Granger causality test is employed to determine the direction of causality between coal consumption and economic growth. The results provide evidence of unidirectional causality from economic growth to coal consumption. This finding implies that coal conservation measures may be implemented with little or no adverse impact on economic growth for the sample countries as a group.

  10. Renewable Energy Consumption and Agriculture: Evidence for Cointegration and Granger causality for Tunisian Economy

    OpenAIRE

    Ben Jebli, Mehdi; Ben Youssef, Slim

    2015-01-01

    This paper uses the vector error correction model (VECM) and Granger causality tests to investigate short and long-run relationships between per capita carbon dioxide (CO2) emissions, real gross domestic product (GDP), renewable and non-renewable energy consumption, trade openness ratio and agricultural value added (AVA) in Tunisia spanning the period 1980-2011. The Johansen-Juselius test shows that all our considered variables are cointegrated. Short-run Granger causality tests reveal the ex...

  11. Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests

    International Nuclear Information System (INIS)

    Narayan, P.K.; Smyth, Russell

    2005-01-01

    This paper examines the relationship between electricity consumption, employment and real income in Australia within a cointegration and causality framework. We find that electricity consumption, employment and real income are cointegrated and that in the long-run employment and real income Granger cause electricity consumption, while in the short run there is weak unidirectional Granger causality running from income to electricity consumption and from income to employment

  12. Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

    Science.gov (United States)

    Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

    2017-04-01

    Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger

  13. Education and Economic Growth in Nigeria: A Granger Causality ...

    African Journals Online (AJOL)

    FIRST LADY

    expenditures on education, primary school enrolment and economic growth. The tests revealed ..... force possessed a positive and significant impact on economic growth through factor ..... Export and Economic Growth in Namibia: A Granger ...

  14. The causal link between energy and output growth: Evidence from Markov switching Granger causality

    International Nuclear Information System (INIS)

    Kandemir Kocaaslan, Ozge

    2013-01-01

    In this paper we empirically investigate the causal link between energy consumption and economic growth employing a Markov switching Granger causality analysis. We carry out our investigation using annual U.S. real GDP, total final energy consumption and total primary energy consumption data which cover the period between 1968 and 2010. We find that there are significant changes in the causal relation between energy consumption and economic growth over the sample period under investigation. Our results show that total final energy consumption and total primary energy consumption have significant predictive content for real economic activity in the U.S. economy. Furthermore, the causality running from energy consumption to output growth seems to be strongly apparent particularly during the periods of economic downturn and energy crisis. We also document that output growth has predictive power in explaining total energy consumption. Furthermore, the power of output growth in predicting total energy consumption is found to diminish after the mid of 1980s. - Highlights: • Total energy consumption has predictive content for real economic activity. • The causality from energy to output growth is apparent in the periods of recession. • The causality from energy to output growth is strong in the periods of energy crisis. • Output growth has predictive power in explaining total energy consumption. • The power of output growth in explaining energy diminishes after the mid of 1980s

  15. Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

    Science.gov (United States)

    Barnett, Lionel; Seth, Anil K

    2017-01-01

    Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity

  16. Electricity supply, employment and real GDP in India: evidence from cointegration and Granger-causality tests

    International Nuclear Information System (INIS)

    Ghosh, Sajal

    2009-01-01

    This study probes nexus between electricity supply, employment and real GDP for India within a multivariate framework using autoregressive distributed lag (ARDL) bounds testing approach of cointegration. Long-run equilibrium relationship has been established among these variables for the time span 1970-71 to 2005-06. The study further establishes long- and short-run Granger causality running from real GDP and electricity supply to employment without any feedback effect. Thus, growth in real GDP and electricity supply are responsible for the high level of employment in India. The absence of causality running from electricity supply to real GDP implies that electricity demand and supply side measures can be adopted to reduce the wastage of electricity, which would not affect future economic growth of India.

  17. Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information

    Science.gov (United States)

    Li, Songting; Xiao, Yanyang; Zhou, Douglas; Cai, David

    2018-05-01

    The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems—it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.

  18. Large-scale Granger causality analysis on resting-state functional MRI

    Science.gov (United States)

    D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

    2016-03-01

    We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

  19. Attention-dependent modulation of cortical taste circuits revealed by Granger causality with signal-dependent noise.

    Directory of Open Access Journals (Sweden)

    Qiang Luo

    2013-10-01

    Full Text Available We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention.

  20. Transfer Entropy for Nonparametric Granger Causality Detection : An Evaluation of Different Resampling Methods

    NARCIS (Netherlands)

    Diks, C.; Fang, H.

    2017-01-01

    The information-theoretical concept transfer entropy is an ideal measure for detecting conditional independence, or Granger causality in a time series setting. The recent literature indeed witnesses an increased interest in applications of entropy-based tests in this direction. However, those tests

  1. Granger causality estimate of information flow in temperature fields is consistent with wind direction

    Czech Academy of Sciences Publication Activity Database

    Jajcay, Nikola; Hlinka, Jaroslav; Hartman, David; Paluš, Milan

    2014-01-01

    Roč. 16, - (2014), EGU2014-12768 ISSN 1607-7962. [EGU General Assembly /11./. 27.04.2014-02.05.2014, Vienna] Institutional support: RVO:67985807 Keywords : Granger causality * climate * information flow * surface air temperature * wind Subject RIV: BB - Applied Statistics, Operational Research

  2. Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

    Science.gov (United States)

    Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

    2018-04-24

    Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

  3. Multivariate Granger causality between electricity generation, exports, prices and GDP in Malaysia

    International Nuclear Information System (INIS)

    Lean, Hooi Hooi; Smyth, Russell

    2010-01-01

    This paper employs annual data for Malaysia from 1970 to 2008 to examine the causal relationship between economic growth, electricity generation, exports and prices in a multivariate model. We find that there is unidirectional Granger causality running from economic growth to electricity generation. However, neither the export-led nor handmaiden theories of trade are supported and there is no causal relationship between prices and economic growth. The policy implication of this result is that electricity conservation policies, including efficiency improvement measures and demand management policies, which are designed to reduce the wastage of electricity and curtail generation can be implemented without having an adverse effect on Malaysia's economic growth. (author)

  4. Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

    Science.gov (United States)

    Liu, Siwei; Molenaar, Peter

    2016-01-01

    This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

  5. Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

    Science.gov (United States)

    DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

    2017-08-01

    Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All

  6. Granger Causality and National Procurement Spending: Applications to the CC130 Hercules Fleet Performance

    Science.gov (United States)

    2011-09-01

    intentionally left blank. ii DRDC CORA TM 2011-154 Dr aft Co py Executive summary Granger Causality and National Procurement Spending David W...à croire que la chaîne d’approvisionnement sous jacente est optimale - la résistance aux chocs de dépenses peut simplement s’expliquer par des stocks...i Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Sommaire

  7. Parametric and nonparametric Granger causality testing: Linkages between international stock markets

    Science.gov (United States)

    De Gooijer, Jan G.; Sivarajasingham, Selliah

    2008-04-01

    This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.

  8. Synergy and redundancy in the Granger causal analysis of dynamical networks

    International Nuclear Information System (INIS)

    Stramaglia, Sebastiano; M Cortes, Jesus; Marinazzo, Daniele

    2014-01-01

    We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy. (paper)

  9. Testing for Nonlinear Granger Causality in the Price-Volume Relations of Taiwan's Stock and Foreign Exchange Markets

    OpenAIRE

    Shyh-Wei Chen; Chun-Wei Chen

    2006-01-01

    This paper investigates the price-volume relationships of Taiwan's stock and foreign exchange markets. We first adopt the traditional linear Granger causality test to achieve this goal. In addition, the nonlinearity feature is also taken into account. We employ the nonlinear Granger causality test, championed by Hiemstra and Jones (1994), to detect the nonlinear relationships among stock and foreign exchange markets. The empirical results show that there do exist nonlinear price-volume relati...

  10. Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

    Science.gov (United States)

    DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

    2017-02-01

    We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

  11. Assessing Granger causality in electrophysiological data: the importance of bipolar derivations

    Directory of Open Access Journals (Sweden)

    Amy eTrongnetrpunya

    2016-01-01

    Full Text Available Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1 laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2 laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3 ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re

  12. Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.

    Science.gov (United States)

    Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E; Ding, Mingzhou

    2015-01-01

    Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

  13. Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

    Science.gov (United States)

    Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

    2016-12-01

    We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

  14. Granger Causalities Between Interest Rate, Price Level, Money Supply and Real Gdp in the Czech Republic

    Directory of Open Access Journals (Sweden)

    Tomáš Urbanovský

    2017-01-01

    Full Text Available The main aim of this paper is to investigate relationships between selected macroeconomic variables – interest rate, price level, money supply and real GDP – in the Czech Republic in order to find out definite implications of its interactions and give recommendations to macroeconomic policy authorities. Two implemented vector autoregression models with different lag length reached slightly different conclusions. VAR(1 suggests that three pairs of Granger causality exist, in particular between price level and interest rate, between real GDP and interest rate and between real GDP and price level. VAR(2 uncovered two more pairs of Granger causality between money supply and interest rate and between money supply and price level. Despite better prediction power of VAR(2 in case of money supply, low correlation coefficient comprising variable money supply raises doubts about the factual existence of causality between money supply and other variables. However, both models allow forecasting the direction of change in case of variables interest rate and real GDP with the same success rate nearly 82 %. Both VARs also agreed that interest rate could be changed by change of price level and that interest rate could be changed by change of real GDP. These conclusions represent potential recommendations to macroeconomic policy authorities. For the purpose of further research, exchange rate variable will be included in the model instead of interest rate, because effect of interest rate turned out to be limited in times of weakened state of Czech economy.

  15. A panel Granger-causality test of endogenous vs. exogenous growth

    OpenAIRE

    Jochen Hartwig

    2009-01-01

    The paper proposes a new test of endogenous vs. exogenous growth theories based on the Granger-causality methodology and applies it to a panel of 20 OECD countries. The test yields divergent evidence with respect to physical and human capital. For physical capital, the test results favor Solow-type exogenous growth theory over AK-type endogenous growth models. On the other hand, the test results lend support to human capital oriented endogenous growth models - like the Uzawa-Lucas model - rat...

  16. Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

    Science.gov (United States)

    Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

    2017-11-01

    Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

  17. Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system.

    Science.gov (United States)

    Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng

    2018-03-01

    Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.

  18. Measures of Coupling between Neural Populations Based on Granger Causality Principle.

    Science.gov (United States)

    Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

    2016-01-01

    This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  19. Measures of coupling between neural populations based on Granger causality principle

    Directory of Open Access Journals (Sweden)

    Maciej Kaminski

    2016-10-01

    Full Text Available This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a weak node. Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

  20. Stock market integration and the speed of information transmission: the role of data frequency in cointegration and Granger causality tests

    Czech Academy of Sciences Publication Activity Database

    Černý, Alexandr; Koblas, M.

    2004-01-01

    Roč. 1, č. 1 (2004), s. 110-120 ISSN 1544-8037 Institutional research plan: CEZ:AV0Z7085904 Keywords : stock market integration * speed of information transmission * data frequency in cointegration and Granger causality tests Subject RIV: AH - Economics

  1. Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

    Science.gov (United States)

    Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

    2012-01-01

    Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

  2. Resting-state brain networks revealed by granger causal connectivity in frogs.

    Science.gov (United States)

    Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

    2016-10-15

    Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Dimension reduction of frequency-based direct Granger causality measures on short time series.

    Science.gov (United States)

    Siggiridou, Elsa; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

    2017-09-01

    The mainstream in the estimation of effective brain connectivity relies on Granger causality measures in the frequency domain. If the measure is meant to capture direct causal effects accounting for the presence of other observed variables, as in multi-channel electroencephalograms (EEG), typically the fit of a vector autoregressive (VAR) model on the multivariate time series is required. For short time series of many variables, the estimation of VAR may not be stable requiring dimension reduction resulting in restricted or sparse VAR models. The restricted VAR obtained by the modified backward-in-time selection method (mBTS) is adapted to the generalized partial directed coherence (GPDC), termed restricted GPDC (RGPDC). Dimension reduction on other frequency based measures, such the direct directed transfer function (dDTF), is straightforward. First, a simulation study using linear stochastic multivariate systems is conducted and RGPDC is favorably compared to GPDC on short time series in terms of sensitivity and specificity. Then the two measures are tested for their ability to detect changes in brain connectivity during an epileptiform discharge (ED) from multi-channel scalp EEG. It is shown that RGPDC identifies better than GPDC the connectivity structure of the simulated systems, as well as changes in the brain connectivity, and is less dependent on the free parameter of VAR order. The proposed dimension reduction in frequency measures based on VAR constitutes an appropriate strategy to estimate reliably brain networks within short-time windows. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. On the dynamics of aggregate output, electricity consumption and exports in Malaysia: Evidence from multivariate Granger causality tests

    International Nuclear Information System (INIS)

    Lean, Hooi Hooi; Smyth, Russell

    2010-01-01

    This paper employs annual data from 1971 to 2006 to examine the causal relationship between aggregate output, electricity consumption, exports, labor and capital in a multivariate model for Malaysia. We find that there is bidirectional Granger causality running between aggregate output and electricity consumption. The policy implication of this result is that Malaysia should adopt the dual strategy of increasing investment in electricity infrastructure and stepping up electricity conservation policies to reduce unnecessary wastage of electricity, in order to avoid the negative effect of reducing electricity consumption on aggregate output. We also find support for the export-led hypothesis which states Granger causality runs from exports to aggregate output. This result is consistent with Malaysia pursuing a successful export-orientated strategy. (author)

  5. State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

    Science.gov (United States)

    Solo, Victor

    2016-05-01

    The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability.

  6. Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

    Science.gov (United States)

    Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

    2007-11-01

    In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

  7. Energy consumption and economic growth for selected OECD countries: Further evidence from the Granger causality test in the frequency domain

    International Nuclear Information System (INIS)

    Bozoklu, Seref; Yilanci, Veli

    2013-01-01

    This paper aims to reexamine the causal relationship between energy consumption and economic growth for 20 OECD countries. To that end, we employ a Granger causality test in the frequency domain which allows us to distinguish short (temporary) and long-run (permanent) causality. The empirical results could be summarized as following. First, in terms of causality running from GDP to energy consumption, there is a temporary relationship for Australia, Austria, Canada, Italy, Japan, Mexico, the Netherlands, Portugal, the UK, the USA, and a permanent relationship for Austria, Belgium, Denmark, Germany, Italy, Japan, the Netherlands, Norway, and the USA. Second, in terms of causality running from energy consumption to GDP, there is a temporary relationship for Austria, Denmark, Italy, the Netherlands, Norway and Portugal, and a permanent relationship for Belgium, Finland, Greece, Italy, Japan, and Portugal. The main implication of our finding is that the energy policies should take into consideration not only the causality direction between economic growth and energy consumption but also whether it is temporal or permanent and furthermore authorities must design policy actions accordingly. - Highlights: • This study reexamines the causal relationship between energy consumption and economic growth. • We employ frequency causality analysis to determine temporary and permanent causality. • The results provide evidence of both temporary and permanent causality relationships for countries examined. • Energy policies should consider whether the causality is temporal or permanent

  8. Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

    Science.gov (United States)

    Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

    2016-01-01

    Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing

  9. On the interpretability and computational reliability of frequency-domain Granger causality [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Luca Faes

    2017-09-01

    Full Text Available This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017. We agree that interpretation issues of Granger causality (GC in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

  10. Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

    Science.gov (United States)

    Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

    2006-06-01

    In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

  11. Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches

    International Nuclear Information System (INIS)

    Shahbaz, Muhammad; Tang, Chor Foon; Shahbaz Shabbir, Muhammad

    2011-01-01

    The aim of this paper is to re-examine the relationship between electricity consumption, economic growth, and employment in Portugal using the cointegration and Granger causality frameworks. This study covers the sample period from 1971 to 2009. We examine the presence of a long-run equilibrium relationship using the bounds testing approach to cointegration within the Unrestricted Error-Correction Model (UECM). Moreover, we examine the direction of causality between electricity consumption, economic growth, and employment in Portugal using the Granger causality test within the Vector Error-Correction Model (VECM). As a summary of the empirical findings, we find that electricity consumption, economic growth, and employment in Portugal are cointegrated and there is bi-directional Granger causality between the three variables in the long-run. With the exception of the Granger causality between electricity consumption and economic growth, the rest of the variables are also bi-directional Granger causality in the short-run. Furthermore, we find that there is unidirectional Granger causality running from economic growth to electricity consumption, but no evidence of reversal causality. - Highlights: → We re-examine the relationship between electricity consumption, economic growth, and employment in Portugal. → The electricity consumption and economic growth is causing each other in the long-run. → In the short-run, economic growth Granger-cause electricity consumption, but no evidence of reversal causality. → Energy conservation policy will deteriorate the process of economic growth in the long-run. → Portugal should increase investment on R and D to design new energy savings technology.

  12. THE GRANGER CAUSALITY TESTS FOR THE FIVE ASEAN COUNTRIES STOCK MARKETS AND MACROECONOMIC VARIABLES DURING AND POST THE 1997 ASIAN FINANCIAL CRISIS

    Directory of Open Access Journals (Sweden)

    Adwin Surja Atmadja

    2005-01-01

    Full Text Available This study seeks to examine the existence of Granger-causality among stock prices indices and macroeconomic variables in five ASEAN countries, Indonesia; Malaysia; the Philippines; Singapore; and Thailand with particular attention to the 1997 Asian financial crisis and period onwards. Using monthly time series data of the countries, a Granger-causality test based on the vector autoregressive (VAR analytical framework was employed to empirically reveal the causality among the variables. This research finds that there were few Granger causalities found between the country's wtock price index and macroeconomic variables. This indicates that the linkages between domestic stock price movements and macroeconomic factors were very. Due to that, the ASEAN stock markets were crelatively unable to efficiently capture changes in economic fundamentals during the observation period in most of the countries in accordance to the literature in emerging stock markets, and that the influence of specific macroeconomic factors on the domestic economies differ across countries. This also implies that the stock markets do not seem to have played a significant role in most countries' economies, and macroeconomic variables are unlikely to be appropriate indicators to predict not only the future behaviour of other macroeconomic variables, but also that of the stock market price indices. Abstract in Bahasa Indonesia : Makalah ini mencoba untuk menganalisis keberadaan Granger-causality antara indeks harga saham dan variabel-variabel ekonomi makro di lima negara ASEAN, yaitu Indonesia; Malaysia; Filipina; Singapore; dan Thailand yang berfokus pada periode terjadinya krisis keuangan Asia pada tahun 1997 dan sesudahnya. Dengan mempergunakan data time series bulanan dari setiap negara tersebut, tes Granger-causality yang didasarkan pada kerangka analisa VAR (vector autoregressive diaplikasikan untuk mengungkap secara empiris hubungan kausal antar variabel. Dari hasil tes

  13. Memory-guided drawing training increases Granger causal influences from the perirhinal cortex to V1 in the blind.

    Science.gov (United States)

    Cacciamani, Laura; Likova, Lora T

    2017-05-01

    The perirhinal cortex (PRC) is a medial temporal lobe structure that has been implicated in not only visual memory in the sighted, but also tactile memory in the blind (Cacciamani & Likova, 2016). It has been proposed that, in the blind, the PRC may contribute to modulation of tactile memory responses that emerge in low-level "visual" area V1 as a result of training-induced cortical reorganization (Likova, 2012, 2015). While some studies in the sighted have indicated that the PRC is indeed structurally and functionally connected to the visual cortex (Clavagnier, Falchier, & Kennedy, 2004; Peterson, Cacciamani, Barense, & Scalf, 2012), the PRC's direct modulation of V1 is unknown-particularly in those who lack the visual input that typically stimulates this region. In the present study, we tested Likova's PRC modulation hypothesis; specifically, we used fMRI to assess the PRC's Granger causal influence on V1 activation in the blind during a tactile memory task. To do so, we trained congenital and acquired blind participants on a unique memory-guided drawing technique previously shown to result in V1 reorganization towards tactile memory representations (Likova, 2012). The tasks (20s each) included: tactile exploration of raised line drawings of faces and objects, tactile memory retrieval via drawing, and a scribble motor/memory control. FMRI before and after a week of the Cognitive-Kinesthetic training on these tasks revealed a significant increase in PRC-to-V1 Granger causality from pre- to post-training during the memory drawing task, but not during the motor/memory control. This increase in causal connectivity indicates that the training strengthened the top-down modulation of visual cortex from the PRC. This is the first study to demonstrate enhanced directed functional connectivity from the PRC to the visual cortex in the blind, implicating the PRC as a potential source of the reorganization towards tactile representations that occurs in V1 in the blind brain

  14. Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies.

    Science.gov (United States)

    Tao, Chenyang; Feng, Jianfeng

    2016-03-15

    Quantifying associations in neuroscience (and many other scientific disciplines) is often challenged by high-dimensionality, nonlinearity and noisy observations. Many classic methods have either poor power or poor scalability on data sets of the same or different scales such as genetical, physiological and image data. Based on the framework of reproducing kernel Hilbert spaces we proposed a new nonlinear association criteria (NAC) with an efficient numerical algorithm and p-value approximation scheme. We also presented mathematical justification that links the proposed method to related methods such as kernel generalized variance, kernel canonical correlation analysis and Hilbert-Schmidt independence criteria. NAC allows the detection of association between arbitrary input domain as long as a characteristic kernel is defined. A MATLAB package was provided to facilitate applications. Extensive simulation examples and four real world neuroscience examples including functional MRI causality, Calcium imaging and imaging genetic studies on autism [Brain, 138(5):13821393 (2015)] and alcohol addiction [PNAS, 112(30):E4085-E4093 (2015)] are used to benchmark NAC. It demonstrates the superior performance over the existing procedures we tested and also yields biologically significant results for the real world examples. NAC beats its linear counterparts when nonlinearity is presented in the data. It also shows more robustness against different experimental setups compared with its nonlinear counterparts. In this work we presented a new and robust statistical approach NAC for measuring associations. It could serve as an interesting alternative to the existing methods for datasets where nonlinearity and other confounding factors are present. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Granger Causality Between The Stock Market Index and Macroeconomic Variables. A Study of Malaysia and Singapore

    OpenAIRE

    Sircar, Shadee Mosaddek

    2009-01-01

    This paper investigates the causal relationships that may be present between the stock market index of developing countries and their macroeconomic variables based on the Vector Error Correction Model (VECM) framework. The countries Malaysia and Singapore are chosen for the purpose of this paper, where FTSE KLCI index and the FTSE STI index are used to represent the stock market performances respectively for each country. The four macroeconomic variables analyzed and used in this paper are Co...

  16. Granger causality mapping during joint actions reveals evidence for forward models that could overcome sensory-motor delays.

    Directory of Open Access Journals (Sweden)

    Idil Kokal

    Full Text Available Studies investigating joint actions have suggested a central role for the putative mirror neuron system (pMNS because of the close link between perception and action provided by these brain regions [1], [2], [3]. In contrast, our previous functional magnetic resonance imaging (fMRI experiment demonstrated that the BOLD response of the pMNS does not suggest that it directly integrates observed and executed actions during joint actions [4]. To test whether the pMNS might contribute indirectly to the integration process by sending information to brain areas responsible for this integration (integration network, here we used Granger causality mapping (GCM [5]. We explored the directional information flow between the anterior sites of the pMNS and previously identified integrative brain regions. We found that the left BA44 sent more information than it received to both the integration network (left thalamus, right middle occipital gyrus and cerebellum and more posterior nodes of the pMNS (BA2. Thus, during joint actions, two anatomically separate networks therefore seem effectively connected and the information flow is predominantly from anterior to posterior areas of the brain. These findings suggest that the pMNS is involved indirectly in joint actions by transforming observed and executed actions into a common code and is part of a generative model that could predict the future somatosensory and visual consequences of observed and executed actions in order to overcome otherwise inevitable neural delays.

  17. Modelling the rand and commodity prices: A Granger causality and cointegration analysis

    Directory of Open Access Journals (Sweden)

    Xolani Ndlovu

    2014-11-01

    Full Text Available This paper examines the ‘commodity currency’ hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.

  18. Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.

    Science.gov (United States)

    Lin, Chin-Teng; Chiu, Te-Cheng; Wang, Yu-Kai; Chuang, Chun-Hsiang; Gramann, Klaus

    2018-01-15

    Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

    Science.gov (United States)

    Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

    2018-02-01

    Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

  20. La inversión extranjera directa y las exportaciones en Tailandia. Pruebas de causalidad de Granger y equilibrio general Foreign direct inverstment and exports in Thailand. granger's causality tests and general equilibrium

    Directory of Open Access Journals (Sweden)

    De Lombaerde Philippe

    1996-12-01

    Full Text Available Los autores investigan la causalidad entre inversión directa y las exportaciones en el caso de Tailandia, un país caracterizado por un rapido crecimiento económico y de exportaciones, y por sus entradas de capital extranjero relativamente importantes. Sus resultados en prueba de causalidad de Granger contradicen los resultados de un estudio de equilibrio general, utilizando datos sobre la inversión japonesa en Tailandia. Los autores apuntan al riesgo de utilizar una sola metodología y sugieren razones por ambigüedad de los resultados.The authors investigate the FDI -export causality in the case of
    Thailand, a country characterized by rapid economic and exports growth, and relatively important incoming FDI flows , Their results from Granger causality tests contradict with those from a general-equilibrium study using data on Japanese FDI in Thailand. The authors indicate the risk of using single methodologies and suggest reasons for the ambiguous
    results.

  1. Theta-rhythmic drive between medial septum and hippocampus in slow-wave sleep and microarousal: a Granger causality analysis.

    Science.gov (United States)

    Kang, D; Ding, M; Topchiy, I; Shifflett, L; Kocsis, B

    2015-11-01

    Medial septum (MS) plays a critical role in controlling the electrical activity of the hippocampus (HIPP). In particular, theta-rhythmic burst firing of MS neurons is thought to drive lasting HIPP theta oscillations in rats during waking motor activity and REM sleep. Less is known about MS-HIPP interactions in nontheta states such as non-REM sleep, in which HIPP theta oscillations are absent but theta-rhythmic burst firing in subsets of MS neurons is preserved. The present study used Granger causality (GC) to examine the interaction patterns between MS and HIPP in slow-wave sleep (SWS, a nontheta state) and during its short interruptions called microarousals (a transient theta state). We found that during SWS, while GC revealed a unidirectional MS→HIPP influence over a wide frequency band (2-12 Hz, maximum: ∼8 Hz), there was no theta peak in the hippocampal power spectra, indicating a lack of theta activity in HIPP. In contrast, during microarousals, theta peaks were seen in both MS and HIPP power spectra and were accompanied by bidirectional GC with MS→HIPP and HIPP→MS theta drives being of equal magnitude. Thus GC in a nontheta state (SWS) vs. a theta state (microarousal) primarily differed in the level of HIPP→MS. The present findings suggest a modification of our understanding of the role of MS as the theta generator in two regards. First, a MS→HIPP theta drive does not necessarily induce theta field oscillations in the hippocampus, as found in SWS. Second, HIPP theta oscillations entail bidirectional theta-rhythmic interactions between MS and HIPP. Copyright © 2015 the American Physiological Society.

  2. Untangling the causal relationship between tax burden distribution and economic growth in 23 OECD countries: Fresh evidence from linear and non-linear Granger causality

    Directory of Open Access Journals (Sweden)

    Sami Saafi

    2017-12-01

    Full Text Available The aim of the paper is to investigate the linear and nonlinear causality between a set of alternative tax burden ratios and economic growth in 23 OECD countries. To that end, the linear causality approach of Toda– Yamamoto (1995 and the nonparametric causality method of Kyrtsou and Labys (2006 are applied to annual data spanning from 1970 to 2014. Results obtained from the nonlinear causality test tend to reject the neutrality hypothesis for the tax structure–growth relationship in 19 of the 23 OECD countries. In the majority of the countries under investigation, the evidence is in line with the growth hypothesis where causality running from economic growth to tax burden ratios was detected in Australia, Denmark, Finland, Japan, New Zealand, and Norway. The opposite causality running from tax structure to economic growth was found in Germany, Netherlands, Portugal, and Sweden. In contrast, the neutrality hypothesis was supported in Austria, Italy, Luxembourg, and the USA, whereas the feedback hypothesis was supported in Turkey and the UK. Additional robustness checks show that when the signs of variations are taken into account, there is an asymmetric causality running from positive tax burden shocks to positive per capita GDP shocks for Belgium, France, and Turkey. Overall, our findings suggest that policy implications of the tax structure-economic growth relationships should be interpreted with caution, taking into account the test-dependent and country-specific results.

  3. Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

    Science.gov (United States)

    Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

    Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes

  4. Testing causal relationships between wholesale electricity prices and primary energy prices

    International Nuclear Information System (INIS)

    Nakajima, Tadahiro; Hamori, Shigeyuki

    2013-01-01

    We apply the lag-augmented vector autoregression technique to test the Granger-causal relationships among wholesale electricity prices, natural gas prices, and crude oil prices. In addition, by adopting a cross-correlation function approach, we test not only the causality in mean but also the causality in variance between the variables. The results of tests using both techniques show that gas prices Granger-cause electricity prices in mean. We find no Granger-causality in variance among these variables. -- Highlights: •We test the Granger-causality among wholesale electricity and primary energy prices. •We test not only the causality in mean but also the causality in variance. •The results show that gas prices Granger-cause electricity prices in mean. •We find no Granger-causality in variance among these variables

  5. [Causal analysis approaches in epidemiology].

    Science.gov (United States)

    Dumas, O; Siroux, V; Le Moual, N; Varraso, R

    2014-02-01

    Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

  6. The Causality between Government Expenditure and Economic Growth in Nigeria: A Toda-Yamamoto Approach

    Directory of Open Access Journals (Sweden)

    Michael Adebayo Ajayi

    2016-01-01

    Full Text Available The relationship between government expenditure and economic growth has been an issue of debate over the years. This study investigates the causality between government expenditure and economic growth in Nigeria between 1985 and 2014. Following the Toda-Yamamoto non-Granger causality testing approach, it finds that government expenditure and economic growth have no causal effect on each other. This offers evidence to invalidate Wagner’s law and the Keynesian proposition in Nigeria. This study recommends that government should strengthen its efforts to curtail corruption as well as introduce stricter checks and controls to reduce or eliminate the profligacy of public funds.

  7. IMPLEMENTING FISCAL OR MONETARY POLICY IN TIME OF CRISIS? RUNNING GRANGER CAUSALITY TO TEST THE PHILLIPS CURVE IN SOME EURO ZONE COUNTRIES

    Directory of Open Access Journals (Sweden)

    Nico Gianluigi

    2014-12-01

    Full Text Available This paper aims to provide empirical evidence about the theoretical relationship between inflation and unemployment in 9 European countries. Based on two major goals for economic policymakers namely, to keep both inflation and unemployment low, we use the ingredients of the Phillips curve to orient fiscal and monetary policies. These policies are prerogative for the achievement of a desirable combination of unemployment and inflation. More in detail, we attempt to address two basic issues. One strand of the study examines the size and sign of the impact of unemployment rate on percentage changes in inflation. In our preferred econometric model, we have made explicit the evidence according to which one unit increase (% in unemployment reduces inflation of roughly 0.73 percent, on average. Next, we turn to the question concerning the causal link between inflation and unemployment and we derive a political framework enables to orient European policymakers in the implementation of either fiscal or monetary policy. In this context, by means of the Granger causality test, we mainly find evidence of a directional causality which runs from inflation to unemployment in 4 out of 9 European countries under analysis. This result implies that political authorities of Austria, Belgium, Germany and Italy should implement monetary policy in order to achieve pre-established targets of unemployment and inflation. In the same context, a directional causality running from unemployment to inflation has been found in France and Cyprus suggesting that a reduction in the unemployment level can be achieved through controlling fiscal policy. However, succeeding in this goal may lead to an increasing demand for goods and services which, in turn, might cause a higher inflation than expected. Finally, while there is no statistical evidence of a causal link between unemployment and inflation in Finland and Greece, a bidirectional causality has been found in Estonia. This

  8. Lexical mediation of phonotactic frequency effects on spoken word recognition: A Granger causality analysis of MRI-constrained MEG/EEG data.

    Science.gov (United States)

    Gow, David W; Olson, Bruna B

    2015-07-01

    Phonotactic frequency effects play a crucial role in a number of debates over language processing and representation. It is unclear however, whether these effects reflect prelexical sensitivity to phonotactic frequency, or lexical "gang effects" in speech perception. In this paper, we use Granger causality analysis of MR-constrained MEG/EEG data to understand how phonotactic frequency influences neural processing dynamics during auditory lexical decision. Effective connectivity analysis showed weaker feedforward influence from brain regions involved in acoustic-phonetic processing (superior temporal gyrus) to lexical areas (supramarginal gyrus) for high phonotactic frequency words, but stronger top-down lexical influence for the same items. Low entropy nonwords (nonwords judged to closely resemble real words) showed a similar pattern of interactions between brain regions involved in lexical and acoustic-phonetic processing. These results contradict the predictions of a feedforward model of phonotactic frequency facilitation, but support the predictions of a lexically mediated account.

  9. Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

    Science.gov (United States)

    Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

    2017-03-01

    Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (pdifferences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

  10. Multivariate granger causality between electricity consumption, exports and GDP: Evidence from a panel of Middle Eastern countries

    International Nuclear Information System (INIS)

    Narayan, Paresh Kumar; Smyth, Russell

    2009-01-01

    This paper examines the causal relationship between electricity consumption, exports and gross domestic product (GDP) for a panel of Middle Eastern countries. We find that for the panel as a whole there are statistically significant feedback effects between these variables. A 1 per cent increase in electricity consumption increases GDP by 0.04 per cent, a 1 per cent increase in exports increases GDP by 0.17 per cent and a 1 per cent increase in GDP generates a 0.95 per cent increase in electricity consumption. The policy implications are that for the panel as a whole these countries should invest in electricity infrastructure and step up electricity conservation policies to avoid a reduction in electricity consumption adversely affecting economic growth. Further policy implications are that for the panel as a whole promoting exports, particularly non-oil exports, is a means to promote economic growth and that expansion of exports can be realized without having adverse effects on energy conservation policies

  11. Control over the Strength of Connections Between Modules: A Double Dissociation Between Stimulus Format and Task Revealed by Granger Causality Mapping in fMRI

    Directory of Open Access Journals (Sweden)

    Britt eAnderson

    2015-03-01

    Full Text Available Drawing on theoretical and computational work with the localist Dual Route reading model and results from behavioral studies, Besner, Moroz, and O'Malley (2011 proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used fMRI to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger Causality Mapping (GCM. Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e., parity judgments of Arabic numerals and reading number words. This frontal region (BA 10 has previously been shown to be involved in goal-directed behaviour and maintenance of a specific task-set. We conclude that overriding stimulus-task defaults requires a modulation of connection strengths between cognitive modules and that the override mechanism predicted from cognitive theory is instantiated by frontal modulation of neural activity of brain regions specialized for sensory processing.

  12. Altered effective connectivity network of the basal ganglia in low-grade hepatic encephalopathy: a resting-state fMRI study with Granger causality analysis.

    Directory of Open Access Journals (Sweden)

    Rongfeng Qi

    Full Text Available BACKGROUND: The basal ganglia often show abnormal metabolism and intracranial hemodynamics in cirrhotic patients with hepatic encephalopathy (HE. Little is known about how the basal ganglia affect other brain system and is affected by other brain regions in HE. The purpose of this study was to investigate whether the effective connectivity network associated with the basal ganglia is disturbed in HE patients by using resting-state functional magnetic resonance imaging (rs-fMRI. METHODOLOGY/PRINCIPAL FINDINGS: Thirty five low-grade HE patients and thirty five age- and gender- matched healthy controls participated in the rs-fMRI scans. The effective connectivity networks associated with the globus pallidus, the primarily affected region within basal ganglia in HE, were characterized by using the Granger causality analysis and compared between HE patients and healthy controls. Pearson correlation analysis was performed between the abnormal effective connectivity and venous blood ammonia levels and neuropsychological performances of all HE patients. Compared with the healthy controls, patients with low-grade HE demonstrated mutually decreased influence between the globus pallidus and the anterior cingulate cortex (ACC, cuneus, bi-directionally increased influence between the globus pallidus and the precuneus, and either decreased or increased influence from and to the globus pallidus in many other frontal, temporal, parietal gyri, and cerebellum. Pearson correlation analyses revealed that the blood ammonia levels in HE patients negatively correlated with effective connectivity from the globus pallidus to ACC, and positively correlated with that from the globus pallidus to precuneus; and the number connectivity test scores in patients negatively correlated with the effective connectivity from the globus pallidus to ACC, and from superior frontal gyrus to globus pallidus. CONCLUSIONS/SIGNIFICANCE: Low-grade HE patients had disrupted effective

  13. Multinational enterprises, foreign direct investment and trade in China : A cointegration and Granger-causality approach

    NARCIS (Netherlands)

    Zhang, Jianhong; Jacobs, Jan; Witteloostuijn, Arjen van

    2004-01-01

    Multinational enterprises (MNEs) play a dominant role in the international business (IB) literature. Traditionally, by far the majority of IB studies deal with issues at the micro level of the individual MNE, or at the meso level of a sample of individual MNEs. This paper focuses on a macro-level

  14. Causality

    Science.gov (United States)

    Pearl, Judea

    2000-03-01

    Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

  15. O aumento da lucratividade expande a acumulação de capital? Uma análise de causalidade de Granger para países da OCDE Does increasing profitability rise capital accumulation? A Granger causality analysis on OECD countries

    Directory of Open Access Journals (Sweden)

    Adalmir Marquetti

    2009-12-01

    Full Text Available O objetivo deste trabalho é testar a hipótese clássico-marxiana de ligação causal entre a taxa de lucro e a taxa de acumulação de capital para um conjunto de 20 países da OCDE. A metodologia utilizada baseia-se no procedimento proposto por Toda e Yamamoto (1995 para testar a hipótese de não causalidade de Granger. A especificação de teste empregada, derivada a partir da equação de Cambridge, envolve três variáveis: a taxa de lucro, a taxa de acumulação e a taxa de investimento. A consideração da variável investimento permite comparações entre as tradições clássico-marxiana e pós-keynesianas. Os resultados para a Austrália, a Dinamarca, os eua, a Finlândia e a Irlanda são consistentes com a concepção clássico-marxiana. Por outro lado, os resultados para o Canadá, a Coreia do Sul, a Grécia e a Suécia são parcialmente consistentes com a tradição pós-keynesiana.The purpose of this paper is to test the classical-Marxian hypothesis of causal linkages between profit rate and the accumulation of capital for a dataset of 20 OECD countries. The procedure proposed by Toda and Yamamoto (1995 to test for the Granger non-causality hypotheses is employed in the statistical procedure. The test specification, derived from the Cambridge equation, involve three variables: profit rate, accumulation of capital and investment rate. The consideration of the investment rate allows a comparison between the classical-Marxian and the post-Keynesian traditions. For the cases of Australia, Denmark, usa, Finland and Ireland, the results provide empirical support for the classical-Marxian conception. On the other hand, in the cases of Canada, South Korea, Greece and Sweden, the results support the post-Keynesian tradition.

  16. Is the tourism-economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top ten tourist destinations

    OpenAIRE

    Shahbaz, Muhammad; Ferrer, Román; Hussain Shahzad, Syed Jawad; Haouas, Ilham

    2017-01-01

    This paper explores the time-varying causal nexus between tourism development and economic growth for the top ten tourist destinations in the world, namely China, France, Germany, Italy, Mexico, the Russian Federation, Spain, Turkey, the United Kingdom and the United States of America, over the period 1990-2015. To that end, a bootstrap rolling window Granger causality approach based on the modified Granger causality test developed by Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996), ...

  17. Bounds test approach to cointegration and causality between nuclear energy consumption and economic growth in India

    International Nuclear Information System (INIS)

    Wolde-Rufael, Yemane

    2010-01-01

    This paper attempts to examine the dynamic relationship between economic growth, nuclear energy consumption, labor and capital for India for the period 1969-2006. Applying the bounds test approach to cointegration developed by we find that there was a short- and a long-run relationship between nuclear energy consumption and economic growth. Using four long-run estimators we also found that nuclear energy consumption has a positive and a statistically significant impact on India's economic growth. Further, applying the approach to Granger causality and the variance decomposition approach developed by , we found a positive and a significant uni-directional causality running from nuclear energy consumption to economic growth without feedback. This implies that economic growth in India is dependent on nuclear energy consumption where a decrease in nuclear energy consumption may lead to a decrease in real income. For a fast growing energy-dependent economy this may have far-reaching implications for economic growth. India's economic growth can be frustrated if energy conservation measures are undertaken without due regard to the negative impact they have on economic growth.

  18. A General Approach to Causal Mediation Analysis

    Science.gov (United States)

    Imai, Kosuke; Keele, Luke; Tingley, Dustin

    2010-01-01

    Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the…

  19. New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

    OpenAIRE

    Gow, David W.; Caplan, David N.

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that...

  20. Delinquency among pathological gamblers: A causal approach.

    Science.gov (United States)

    Meyer, G; Fabian, T

    1992-03-01

    In a comprehensive research project on gamblers in self-help groups in West Germany one object of investigation was the question of whether or not pathological gambling has a criminogenic effect. 54.5% of the 437 members of Gamblers Anonymous interviewed stated that they had committed illegal actions in order to obtain money for gambling. Comparisons of this sub-group with those interviewees who did not admit having committed criminal offences show distinct differences: Those who admitted illegal action were more excessive in their gambling behavior and experienced a higher degree of subjective satisfaction through gambling. They also showed a more pronounced problem behavior and more psychosocial problems because of gambling. A multiple regression within the framework of path analysis was computed in order to explore causal links between pathological gambling and delinquency. The results support the hypothesis that pathological gambling can lead to delinquent behavior. Forensic implications are discussed.

  1. A Bayesian nonparametric approach to causal inference on quantiles.

    Science.gov (United States)

    Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

    2018-02-25

    We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

  2. Moment problems and the causal set approach to quantum gravity

    International Nuclear Information System (INIS)

    Ash, Avner; McDonald, Patrick

    2003-01-01

    We study a collection of discrete Markov chains related to the causal set approach to modeling discrete theories of quantum gravity. The transition probabilities of these chains satisfy a general covariance principle, a causality principle, and a renormalizability condition. The corresponding dynamics are completely determined by a sequence of non-negative real coupling constants. Using techniques related to the classical moment problem, we give a complete description of any such sequence of coupling constants. We prove a representation theorem: every discrete theory of quantum gravity arising from causal set dynamics satisfying covariance, causality, and renormalizability corresponds to a unique probability distribution function on the non-negative real numbers, with the coupling constants defining the theory given by the moments of the distribution

  3. The causal approach in quantum field theory

    International Nuclear Information System (INIS)

    Grigore, D. R.

    2003-01-01

    The mathematical formulation of perturbative renormalization theory starts from Bogoliubov axioms imposed on the S-matrix (or equivalently on the chronological products). The S-matrix is a formal series of operator valued distributions: these distributions are denoted by T(x 1 , ... , x n ) and one supposes that they act in the Fock space of some collection of free fields. These operator-valued distributions are called chronological products. The expression T(x) is called the interaction Lagrangian. It is convenient to construct more general objects namely, the operator-valued distributions T(W 1 (x 1 ), ... ,W n (x n )), where W j are arbitrary Wick monomials. These objects verify some properties (following from Bogolyubov axioms) and express the following properties: the initial condition, skew-symmetry in all arguments, Poincare invariance, causality and unitarity. The existence of solutions follows from the analysis of Epstein and Glaser as a recursive procedure using in an essential way the causality axiom. Sometimes it is possible to supplement these axioms by other invariance properties with respect to space-time symmetries (inversions and/or scale invariance), charge conjugation, global symmetry with respect to some internal symmetry group, supersymmetric invariance, etc. if they are valid for the interaction Lagrangian. In the literature, the invariance properties of the chronological products with respect to scale invariance was analyzed in detail. The scale invariance operators U λ are transforming field operators corresponding to particles of masses m j in fields corresponding to scaled masses λ -1 m j . One can prove that if all masses are positive the chronological products can be normalized such that they are scale invariant. On the contrary, if all masses of the model are zero then the scale invariance of the chronological products can be implemented only up to some logarithmic terms in λ. For models describing higher spin particles unphysical

  4. The predictive power of the business and bank sentiment of firms : A high-dimensional Granger causality approach

    NARCIS (Netherlands)

    Wilms, I.; Gelper, S.E.C.; Croux, C.

    2016-01-01

    We study the predictive power of industry-specific economic sentiment indicators for future macro-economic developments. In addition to the sentiment of firms towards their own business situation, we study their sentiment with respect to the banking sector – their main credit providers. The use of

  5. A multivariate analysis of the causal flow between renewable energy consumption and GDP in Tunisia

    OpenAIRE

    Ben Salha, Ousama; Sebri, Maamar

    2013-01-01

    This paper examines the causality linkages between economic growth, renewable energy consumption, CO2 emissions and domestic investment in Tunisia between 1971 and 2010. Using the ARDL bounds testing approach to cointegration, long-run relationships between the variables are identified. On the other hand, the Granger causality analysis indicates that there is bi-directional causality between renewable energy consumption and economic growth, which supports the validity of the feedback hypothes...

  6. Causal approach to (2+1)-dimensional Quantum Electrodynamics

    International Nuclear Information System (INIS)

    Scharf, G.; Wreszinski, W.F.; Pimentel, B.M.; Tomazelli, J.L.

    1993-05-01

    It is shown that the causal approach to (2+1)-dimensional quantum electrodynamics yields a well-defined perturbative theory. In particular, and in contrast to renormalized perturbative quantum field theory, it is free of any ambiguities and ascribes a nonzero value to the dynamically generated, nonperturbative photon mass. (author). 12 refs

  7. CAUSAL RELATIONSHIP BETWEEN FOSSIL FUEL CONSUMPTION AND ECONOMIC GROWTH IN JAPAN: A MULTIVARIATE APPROACH

    Directory of Open Access Journals (Sweden)

    Hazuki Ishida

    2013-01-01

    Full Text Available This paper explores whether Japanese economy can continue to grow without extensive dependence on fossil fuels. The paper conducts time series analysis using a multivariate model of fossil fuels, non-fossil energy, labor, stock and GDP to investigate the relationship between fossil fuel consumption and economic growth in Japan. The results of cointegration tests indicate long-run relationships among the variables. Using a vector error-correction model, the study reveals bidirectional causality between fossil fuels and GDP. The results also show that there is no causal relationship between non-fossil energy and GDP. The results of cointegration analysis, Granger causality tests, and variance decomposition analysis imply that non-fossil energy may not necessarily be able to play the role of fossil fuels. Japan cannot seem to realize both continuous economic growth and the departure from dependence on fossil fuels. Hence, growth-oriented macroeconomic policies should be re-examined.

  8. Causal relationship between trade openness, economic growth and energy consumption: A panel data analysis of Asian countries

    International Nuclear Information System (INIS)

    Nasreen, Samia; Anwar, Sofia

    2014-01-01

    This paper explores the causal relationship between economic growth, trade openness and energy consumption using data of 15 Asian countries. The study covers the period of 1980–2011. We have applied panel cointegration and causality approaches to examine the long-run and causal relationship between variables. Empirical results confirm the presence of cointegration between variables. The impact of economic growth and trade openness on energy consumption is found to be positive. The panel Granger causality analysis reveals the bidirectional causality between economic growth and energy consumption, trade openness and energy consumption. - Highlights: • This study analyzes causality between energy, growth and trade in the Asian region. • Empirical results supported cointegrating relationship between variables. • Positive impact of growth and trade openness on energy usage is found in the long run. • Bidirectional Granger causality is observed between selected variables in the long run

  9. Does electricity consumption panel Granger cause GDP? A new global evidence

    Energy Technology Data Exchange (ETDEWEB)

    Narayan, Paresh Kumar [School of Accounting, Economics and Finance, Faculty of Business and Economics, Deakin University, Melbourne (Australia); Narayan, Seema [School of Economics, Finance, and Marketing, RMIT University, Melbourne (Australia); Popp, Stephan [Department of Economics, University of Duisburg-Essen (Germany)

    2010-10-15

    The goal of this paper is to undertake a panel data investigation of long-run Granger causality between electricity consumption and real GDP for seven panels, which together consist of 93 countries. We use a new panel causality test and find that in the long-run both electricity consumption and real GDP have a bidirectional Granger causality relationship except for the Middle East where causality runs only from GDP to electricity consumption. Finally, for the G6 panel the estimates reveal a negative sign effect, implying that increasing electricity consumption in the six most industrialised nations will reduce GDP. (author)

  10. Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

    Science.gov (United States)

    Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

    2017-05-01

    This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO 2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.

  11. Robust, Causal, and Incremental Approaches to Investigating Linguistic Adaptation

    Science.gov (United States)

    Roberts, Seán G.

    2018-01-01

    This paper discusses the maximum robustness approach for studying cases of adaptation in language. We live in an age where we have more data on more languages than ever before, and more data to link it with from other domains. This should make it easier to test hypotheses involving adaptation, and also to spot new patterns that might be explained by adaptation. However, there is not much discussion of the overall approach to research in this area. There are outstanding questions about how to formalize theories, what the criteria are for directing research and how to integrate results from different methods into a clear assessment of a hypothesis. This paper addresses some of those issues by suggesting an approach which is causal, incremental and robust. It illustrates the approach with reference to a recent claim that dry environments select against the use of precise contrasts in pitch. Study 1 replicates a previous analysis of the link between humidity and lexical tone with an alternative dataset and finds that it is not robust. Study 2 performs an analysis with a continuous measure of tone and finds no significant correlation. Study 3 addresses a more recent analysis of the link between humidity and vowel use and finds that it is robust, though the effect size is small and the robustness of the measurement of vowel use is low. Methodological robustness of the general theory is addressed by suggesting additional approaches including iterated learning, a historical case study, corpus studies, and studying individual speech. PMID:29515487

  12. The causal nexus between carbon dioxide emissions and agricultural ecosystem-an econometric approach.

    Science.gov (United States)

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2017-01-01

    Achieving a long-term food security and preventing hunger include a better nutrition through sustainable systems of production, distribution, and consumption. Nonetheless, the quest for an alternative to increasing global food supply to meet the growing demand has led to the use of poor agricultural practices that promote climate change. Given the contribution of the agricultural ecosystem towards greenhouse gas (GHG) emissions, this study investigated the causal nexus between carbon dioxide emissions and agricultural ecosystem by employing a data spanning from 1961 to 2012. Evidence from long-run elasticity shows that a 1 % increase in the area of rice paddy harvested will increase carbon dioxide emissions by 1.49 %, a 1 % increase in biomass-burned crop residues will increase carbon dioxide emissions by 1.00 %, a 1 % increase in cereal production will increase carbon dioxide emissions by 1.38 %, and a 1 % increase in agricultural machinery will decrease carbon dioxide emissions by 0.09 % in the long run. There was a bidirectional causality between carbon dioxide emissions, cereal production, and biomass-burned crop residues. The Granger causality shows that the agricultural ecosystem in Ghana is sensitive to climate change vulnerability.

  13. Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien; Tsai, Chung-Ming

    2011-01-01

    This paper addresses the impact of both economic growth and financial development on environmental degradation using a panel cointegration technique for the period between 1980 and 2007, except for Russia (1992-2007). In long-run equilibrium, CO 2 emissions appear to be energy consumption elastic and FDI inelastic, and the results seem to support the Environmental Kuznets Curve (EKC) hypothesis. The causality results indicate that there exists strong bidirectional causality between emissions and FDI and unidirectional strong causality running from output to FDI. The evidence seems to support the pollution haven and both the halo and scale effects. Therefore, in attracting FDI, developing countries should strictly examine the qualifications for foreign investment or to promote environmental protection through the coordinated know-how and technological transfer with foreign companies to avoid environmental damage. Additionally, there exists strong output-emissions and output-energy consumption bidirectional causality, while there is unidirectional strong causality running from energy consumption to emissions. Overall, the method of managing both energy demand and FDI and increasing both investment in the energy supply and energy efficiency to reduce CO 2 emissions and without compromising the country's competitiveness can be adopted by energy-dependent BRIC countries.

  14. Confounding effects of phase delays on causality estimation.

    Directory of Open Access Journals (Sweden)

    Vasily A Vakorin

    Full Text Available Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects.

  15. A developmental approach to learning causal models for cyber security

    Science.gov (United States)

    Mugan, Jonathan

    2013-05-01

    To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

  16. The causal relationship between Foreign Direct Investment (FDI ...

    African Journals Online (AJOL)

    The causal relationship between Foreign Direct Investment (FDI) and the ... of selected west African countries: Panel ARDL/Granger Causality Analysis. ... among this developing countries and an important revelation for policy implication.

  17. An Information Processing Approach to Children's Causal Reasoning.

    Science.gov (United States)

    Siegler, Robert S.

    This paper questions evidence for the thesis that causal reasoning of older children is more logical than that of younger ones, and describes two experiments which attempted to determine (1) whether there are true developmental differences in causal reasoning, and (2) what explanations for developmental differences can be supported. In the first…

  18. Temporal causality between house prices and output in the U.S.: a bootstrap rolling-window approach

    CSIR Research Space (South Africa)

    Nyakabawo, W

    2015-07-01

    Full Text Available . Using quarterly data for all 379 metropolitan statistic areas (MSAs) in the U.S. from 1980:1 to 2008:2, Miller et al., (2011) empirically study the effect of house prices on local Gross Metropolitan Product (GMP). The paper concluded that house price... for lagged values of y . The Granger non-causality hypothesis tests a joint restriction, using the Wald, Lagrange multiplier (LM), and likelihood ratio (LR) statistics. These test statistics require stationary time series. If stationarity does not hold...

  19. Renormalization group approach to causal bulk viscous cosmological models

    International Nuclear Information System (INIS)

    Belinchon, J A; Harko, T; Mak, M K

    2002-01-01

    The renormalization group method is applied to the study of homogeneous and flat Friedmann-Robertson-Walker type universes, filled with a causal bulk viscous cosmological fluid. The starting point of the study is the consideration of the scaling properties of the gravitational field equations, the causal evolution equation of the bulk viscous pressure and the equations of state. The requirement of scale invariance imposes strong constraints on the temporal evolution of the bulk viscosity coefficient, temperature and relaxation time, thus leading to the possibility of obtaining the bulk viscosity coefficient-energy density dependence. For a cosmological model with bulk viscosity coefficient proportional to the Hubble parameter, we perform the analysis of the renormalization group flow around the scale-invariant fixed point, thereby obtaining the long-time behaviour of the scale factor

  20. Causality between regional stock markets: A frequency domain approach

    Directory of Open Access Journals (Sweden)

    Gradojević Nikola

    2013-01-01

    Full Text Available Using a data set from five regional stock exchanges (Serbia, Croatia, Slovenia, Hungary and Germany, this paper presents a frequency domain analysis of a causal relationship between the returns on the CROBEX, SBITOP, CETOP and DAX indices, and the return on the major Serbian stock exchange index, BELEX 15. We find evidence of a somewhat dominant effect of the CROBEX and CETOP stock indices on the BELEX 15 stock index across a range of frequencies. The results also indicate that the BELEX 15 index and the SBITOP index interact in a bi-directional causal fashion. Finally, the DAX index movements consistently drive the BELEX 15 index returns for cycle lengths between 3 and 11 days without any feedback effect.

  1. Scientific realism in particle physics a causal approach

    CERN Document Server

    Egg, Matthias

    2014-01-01

    Does particle physics really describe the basic constituents of the material world or is it just a useful tool for deriving empirical predictions? This book proposes a novel answer to that question, emphasizing the importance of causal reasoning for the justification of scientific claims. It thereby responds to general worries about scientific realism as well as to more specific challenges stemming from the interpretation of quantum physics.

  2. Causal Relationships in the Balanced Scorecard: A Path Analysis Approach

    OpenAIRE

    Yael Perlman

    2013-01-01

    We use path analysis to identify causal relationships between different performance measures in each of the four perspectives defined in the balanced scorecard and examine the influence of time lag on relationships between perspectives. We analyze performance data from a real high-tech company. Our results point to a direct relationship between leading measures in the learning and growth perspective and lagging measures in the financial perspective. Our findings also support the existence of ...

  3. The Causal Relationship between Health and Education Expenditures in Malaysia

    Directory of Open Access Journals (Sweden)

    Chor Foon TANG

    2011-08-01

    Full Text Available A major macroeconomic policy in generating economic growth is to encourage investments on human capital such as health and education. This is because both health and education make significant contribution to increasing productivity of the labour force which ultimately exerts a positive effect on raising output levels. A question that arises is whether investments on health and education have a causal relationship and if so, what is the directional causality? The objective of this study is to examine the causal relationship between health and education expenditures in Malaysia. This study covered annual data from 1970 to 2007. Using Granger causality as well as Toda and Yamamoto MWALD causality approaches, this study suggests that education Granger-causes health expenditure in both the short run and long run. The findings of this study implied that the Malaysian society places preference on education expenditure rather than health. This preference is not unexpected as generally, an educated and knowledgeable society precedes a healthy one. Before a society has attained a relatively higher level of education, it is less aware of the importance of health. Thus, expenditure on education should lead expenditure on health.

  4. Renewable energy consumption and economic growth in nine OECD countries: bounds test approach and causality analysis.

    Science.gov (United States)

    Hung-Pin, Lin

    2014-01-01

    The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries-United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.

  5. Causality relationship between the price of oil and economic growth in Japan

    International Nuclear Information System (INIS)

    Hanabusa, Kunihiro

    2009-01-01

    This paper investigates the relationship between the price of oil and economic growth in Japan during the period from 2000 to 2008 using an exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model. We employ a residual cross-correlation function (CCF) approach developed by [Cheung, Y.W., Ng, N.K., 1996. A causality-in-variance test and its application to financial market prices. Journal of Econometrics 72, 33-48]. The empirical results reveal that the economic growth rate Granger-causes the change of oil price in mean and variance and the change of oil price Granger-causes the economic growth rate in mean and variance. Previous studies have analyzed the response of economic activity to oil price shocks. However, we analyze the causality relations for both means and variances, and identify the direction of information flow and the timing of causation. (author)

  6. Impact of Systematic Sampling on Causality in the presence of Unit Roots

    OpenAIRE

    Rajaguru GULASEKARAN

    2002-01-01

    Quite contrary to the stationary case where systematic sampling preserves the direction of Granger causality, this paper shows that systematic sampling of integrated series may induce spurious causality, even if they are used in differenced form.

  7. Exploring manual asymmetries during grasping: a dynamic causal modeling approach.

    Directory of Open Access Journals (Sweden)

    Chiara eBegliomini

    2015-02-01

    Full Text Available Recording of neural activity during grasping actions in macaques showed that grasp-related sensorimotor transformations are accomplished in a circuit constituted by the anterior part of the intraparietal sulcus (AIP, the ventral (F5 and the dorsal (F2 region of the premotor area. In humans, neuroimaging studies have revealed the existence of a similar circuit, involving the putative homolog of macaque areas AIP, F5 and F2. These studies have mainly considered grasping movements performed with the right dominant hand and only a few studies have measured brain activity associated with a movement performed with the left non-dominant hand. As a consequence of this gap, how the brain controls for grasping movement performed with the dominant and the non-dominant hand still represents an open question. A functional resonance imaging experiment (fMRI has been conducted, and effective connectivity (Dynamic Causal Modelling, DCM was used to assess how connectivity among grasping-related areas is modulated by hand (i.e., left and right during the execution of grasping movements towards a small object requiring precision grasping. Results underlined boosted inter-hemispheric couplings between dorsal premotor cortices during the execution of movements performed with the left rather than the right dominant hand. More specifically, they suggest that the dorsal premotor cortices may play a fundamental role in monitoring the configuration of fingers when grasping movements are performed by either the right and the left hand. This role becomes particularly evident when the hand less-skilled (i.e., the left hand to perform such action is utilized. The results are discussed in light of recent theories put forward to explain how parieto-frontal connectivity is modulated by the execution of prehensile movements.

  8. IMPACT OF TRADE OPENNESS ON OUTPUT GROWTH: CO INTEGRATION AND ERROR CORRECTION MODEL APPROACH

    Directory of Open Access Journals (Sweden)

    Asma Arif

    2012-01-01

    Full Text Available This study analyzed the long run relationship between trade openness and output growth for Pakistan using annual time series data for 1972-2010. This study follows the Engle and Granger co integration analysis and error correction approach to analyze the long run relationship between the two variables. The Error Correction Term (ECT for output growth and trade openness is significant at 5% level of significance and indicates a positive long run relation between the variables. This study has also analyzed the causality between trade openness and output growth by using granger causality test. The results of granger causality show that there is a bi-directional significant relationship between trade openness and economic growth.

  9. A Systematic Approach to Cultural Explanations of War: Tracing Causal Processes in Two West African Insurgencies

    NARCIS (Netherlands)

    Richards, P.

    2011-01-01

    Many accounts of cultural factors in armed conflicts are dependent on circumstantial details. Alternative quantitative approaches suffer from confusion of correlation and cause. This paper describes and exemplifies a third approach to the analysis of cultural factors in war—causal process tracing.

  10. Model selection approach suggests causal association between 25-hydroxyvitamin D and colorectal cancer.

    Directory of Open Access Journals (Sweden)

    Lina Zgaga

    Full Text Available Vitamin D deficiency has been associated with increased risk of colorectal cancer (CRC, but causal relationship has not yet been confirmed. We investigate the direction of causation between vitamin D and CRC by extending the conventional approaches to allow pleiotropic relationships and by explicitly modelling unmeasured confounders.Plasma 25-hydroxyvitamin D (25-OHD, genetic variants associated with 25-OHD and CRC, and other relevant information was available for 2645 individuals (1057 CRC cases and 1588 controls and included in the model. We investigate whether 25-OHD is likely to be causally associated with CRC, or vice versa, by selecting the best modelling hypothesis according to Bayesian predictive scores. We examine consistency for a range of prior assumptions.Model comparison showed preference for the causal association between low 25-OHD and CRC over the reverse causal hypothesis. This was confirmed for posterior mean deviances obtained for both models (11.5 natural log units in favour of the causal model, and also for deviance information criteria (DIC computed for a range of prior distributions. Overall, models ignoring hidden confounding or pleiotropy had significantly poorer DIC scores.Results suggest causal association between 25-OHD and colorectal cancer, and support the need for randomised clinical trials for further confirmations.

  11. Sustainability Reporting Experience by Universities: A Causal Configuration Approach

    Science.gov (United States)

    Zorio-Grima, Ana; Sierra-García, Laura; Garcia-Benau, Maria A.

    2018-01-01

    Purpose: The purpose of this research is to identify the combinations of factors leading to experience in sustainability reporting by Spanish public universities. Design/methodology/approach: Using a sample of 49 public universities in Spain, this paper identifies the combinations of factors on innovation profile, political and internal factors…

  12. Approach to the nonrelatiVistic scattering theory based on the causality superposition and unitarity principles

    International Nuclear Information System (INIS)

    Gajnutdinov, R.Kh.

    1983-01-01

    Possibility is studied to build the nonrelativistic scattering theory on the base of the general physical principles: causality, superposition, and unitarity, making no use of the Schroedinger formalism. The suggested approach is shown to be more general than the nonrelativistic scattering theory based on the Schroedinger equation. The approach is applied to build a model ofthe scattering theory for a system which consists of heavy nonrelativistic particles and a light relativistic particle

  13. An Empirical Analysis of the ASEAN-4’s Causality between Exports and Output Growth

    OpenAIRE

    Lam, Tri Dung

    2016-01-01

    This paper specifically focuses on analysing the causality between real GDP and real export of goods and services of the ASEAN-4 countries (Indonesia, Malaysia, Thailand and the Philippines) by using comprehensive econometric techniques such as the unit root test, cointegration test, and error correction model. This study reveals that for short-run dynamics, while bi-directional Granger-causality exists in Malaysia, the Philippines, and Thailand; the unidirectional Granger-causality runs from...

  14. Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

    Science.gov (United States)

    Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

    2015-12-01

    This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

  15. A study on the causal effect of urban population growth and international trade on environmental pollution: evidence from China.

    Science.gov (United States)

    Boamah, Kofi Baah; Du, Jianguo; Boamah, Angela Jacinta; Appiah, Kingsley

    2018-02-01

    This study seeks to contribute to the recent literature by empirically investigating the causal effect of urban population growth and international trade on environmental pollution of China, for the period 1980-2014. The Johansen cointegration confirmed a long-run cointegration association among the utilised variables for the case of China. The direction of causality among the variables was, consequently, investigated using the recent bootstrapped Granger causality test. This bootstrapped Granger causality approach is preferred as it provides robust and accurate critical values for statistical inferences. The findings from the causality analysis revealed the existence of a bi-directional causality between import and urban population. The three most paramount variables that explain the environmental pollution in China, according to the impulse response function, are imports, urbanisation and energy consumption. Our study further established the presence of an N-shaped environmental Kuznets curve relationship between economic growth and environmental pollution of China. Hence, our study recommends that China should adhere to stricter environmental regulations in international trade, as well as enforce policies that promote energy efficiency in the urban residential and commercial sector, in the quest to mitigate environmental pollution issues as the economy advances.

  16. New levels of language processing complexity and organization revealed by Granger causation

    Directory of Open Access Journals (Sweden)

    David W Gow

    2012-11-01

    Full Text Available Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all nonredundant potentially interacting signals, and has shown that even early processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of language-specific localized processes.

  17. New levels of language processing complexity and organization revealed by granger causation.

    Science.gov (United States)

    Gow, David W; Caplan, David N

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even "early" processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of "language-specific" localized processes.

  18. Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

    Directory of Open Access Journals (Sweden)

    Moura LMVR

    2016-12-01

    Full Text Available Lidia MVR Moura,1,2 M Brandon Westover,1,2 David Kwasnik,1 Andrew J Cole,1,2 John Hsu3–5 1Massachusetts General Hospital, Department of Neurology, Epilepsy Service, Boston, MA, USA; 2Harvard Medical School, Boston, MA, USA; 3Massachusetts General Hospital, Mongan Institute, Boston, MA, USA; 4Harvard Medical School, Department of Medicine, Boston, MA, USA; 5Harvard Medical School, Department of Health Care Policy, Boston, MA, USA Abstract: The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer’s disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions. Keywords: epilepsy, epidemiology, neurostatistics, causal inference

  19. Causality Between Urban Concentration and Environmental Quality

    Directory of Open Access Journals (Sweden)

    Amin Pujiati

    2015-08-01

    Full Text Available Population is concentrated in urban areas can cause the external diseconomies on environment if it exceeds the carrying capacity of the space and the urban economy. Otherwise the quality of the environment is getting better, led to the concentration of population in urban areas are increasingly high. This study aims to analyze the relationship of causality between the urban concentration and environmental quality in urban agglomeration areas. The data used in the study of secondary data obtained from the Central Bureau of statistics and the City Government from 2000 to 2013. The analytical method used is the Granger causality and descriptive. Granger causality study results showed no pattern of reciprocal causality, between urban concentration and the quality of the environment, but there unidirectional relationship between the urban concentration and environmental quality. This means that increasing urban concentration led to decreased environmental quality.

  20. Investigating the causal effect of vitamin D on serum adiponectin using a mendelian randomization approach

    DEFF Research Database (Denmark)

    Husemoen, L. L. N.; Skaaby, T.; Martinussen, Torben

    2014-01-01

    Background/Objectives: The aim was to examine the causal effect of vitamin D on serum adiponectin using a multiple instrument Mendelian randomization approach. Subjects/Methods: Serum 25-hydroxy vitamin D (25(OH)D) and serum total or high molecular weight (HMW) adiponectin were measured in two...... doubling of 25(OH)D was 4.78, 95% CI: 1.96, 7.68, Pvitamin D-binding protein gene and the filaggrin gene as instrumental variables, the causal effect in % was estimated to 61.46, 95% CI: 17.51, 120.28, P=0.003 higher adiponectin per doubling of 25(OH)D. In the MONICA10...... effect estimate in % per doubling of 25(OH)D was 37.13, 95% CI:-3.67, 95.20, P=0.080). Conclusions: The results indicate a possible causal association between serum 25(OH)D and total adiponectin. However, the association was not replicated for HMW adiponectin. Thus, further studies are needed to confirm...

  1. Corruption, Political Instability and Economic Development in the Economic Community of West African States (ECOWAS): Is There a Causal Relationship?

    OpenAIRE

    Nurudeen Abu; Mohd Zaini Abd Karim; Mukhriz Izraf Azman Aziz

    2015-01-01

    Despite the abundant research on economic development, corruption and political instability, little research has attempted to examine whether there is a causal relationship among them. This paper examines the causal relationship among corruption, political instability and economic development in the ECOWAS using the Granger causality test within a multivariate cointegration and error-correction framework for the 1996 - 2012 period. The findings indicate that political instability Granger-caus...

  2. Causality between government revenue and expenditure in Malaysia: A seasonal cointegration test

    OpenAIRE

    Goh, Soo Khoon; Dawood, Mithani

    1999-01-01

    The objective of this article is to empirically incorporate the effect of seasonality in examining the causal relationship between quarterly government revenue and governemnt expenditure in Malaysia for the period 1970.1- 1994.4. The seasonal integration and cointegration tests developed by Hylleberg, Engle, Granger and Yoo (1990) and extended by Engle, Granger, Hylleberg and lee (1993) are applied prior to determination of causality. Evidence of seasonal cointegration of biaanual frequency ...

  3. “Finance-Growth-Crisis Nexus in India: Evidence from Cointegration and Causality Assessment” - L’interazione finanza-crescita-crisi in India: evidenze da una analisi di cointegrazione e causalità

    OpenAIRE

    Fukuda, Takashi; Dahalan, Jauhari

    2011-01-01

    This paper attempts to explore a new dimension of India’s ‘financegrowth- crisis’ nexus. For this end, the summary indicators of financial development, financial crisis and financial repression are created through the principal component approach, and we perform the cointegration and Granger causality analysis employing the methods of vector error correction model (VECM) and autoregressive distributed lag (ARDL). The element of structural break is also taken into assessment while specifying t...

  4. R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis

    Directory of Open Access Journals (Sweden)

    Stephan J Ritter

    2014-04-01

    Full Text Available We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including super learner, a meta-learner which combines several different algorithms into one. We describe a simulation in which the double robust TMLE is compared to the graphical computation estimator. We also provide example analyses using two data sets which are included with the package.

  5. Dynamics of charged viscous dissipative cylindrical collapse with full causal approach

    Energy Technology Data Exchange (ETDEWEB)

    Shah, S.M.; Abbas, G. [The Islamia University of Bahawalpur, Department of Mathematics, Bahawalpur (Pakistan)

    2017-11-15

    The aim of this paper is to investigate the dynamical aspects of a charged viscous cylindrical source by using the Misner approach. To this end, we have considered the more general charged dissipative fluid enclosed by the cylindrical symmetric spacetime. The dissipative nature of the source is due to the presence of dissipative variables in the stress-energy tensor. The dynamical equations resulting from such charged cylindrical dissipative source have been coupled with the causal transport equations for heat flux, shear and bulk viscosity, in the context of the Israel-Steward theory. In this case, we have the considered Israel-Steward transportation equations without excluding the thermodynamics viscous/heat coupling coefficients. The results are compared with the previous works in which such coefficients were excluded and viscosity variables do not satisfy the casual transportation equations. (orig.)

  6. How applicable is export-led growth and import-led growth hypotheses to South African economy? The VECM and causality approach

    Directory of Open Access Journals (Sweden)

    Ntebogang Dinah Moroke

    2015-04-01

    Full Text Available This paper investigated exports, imports and the economic growth nexus in the context of South Africa. The paper sets out to examine if long-run and causal relationships exist between these variables. Quarterly time series data ranging between 1998 and 2013 obtained from the South African Reserve Bank and Quantec databases was employed. Initial data analysis proved that the variables are integrated at their levels. The results further indicated that exports, imports and economic growth are co-integrated, confirming an existence of a long-run equilibrium relationship. Granger causal results were shown running from exports and imports to GDP and from imports to exports, validating export-led and import-led growth hypotheses in South Africa. A significant causality running from imports to exports, suggests that South Africa imported finished goods in excess. If this is not avoided, lots of problems could be caused. A suggestion was made to avoid such problematic issues as they may lead to replaced domestic output and displacement of employees. Another dreadful ramification may be an adverse effect on the economy which may further be experienced in the long-run

  7. Epidemiological causality.

    Science.gov (United States)

    Morabia, Alfredo

    2005-01-01

    Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.

  8. A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

    Science.gov (United States)

    Faes, Luca; Erla, Silvia; Porta, Alberto; Nollo, Giandomenico

    2013-08-28

    We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

  9. Bi-directional causality in California's electricity and natural-gas markets

    International Nuclear Information System (INIS)

    Woo, Chi-Keung; Olson, Arne; Horowitz, Ira; Luk, Stephen

    2006-01-01

    The Granger instantaneous-causality test is applied to explore the potential causal relationships between wholesale electricity and natural-gas prices in California. The test shows these relationships to be bi-directional, and reveals California's electricity and natural-gas markets to be as inextricably intertwined as casual observation and theoretical considerations would suggest they ought to be. This meshing of markets exacerbated the effects of California's natural-gas crisis on the contemporaneous electricity crisis, while concurrently the electricity crisis may have contributed to the dysfunction in the national-gas market and helped to precipitate the natural-gas crisis. The finding supports an integrated approach, as opposed to a piecemeal approach, for formulating energy policy recommendations, not just in California but in the world at large

  10. Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference.

    Science.gov (United States)

    Morabia, Alfredo

    2013-11-15

    The epidemiologic approach to causal inference (i.e., Hill's viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It does not involve statements of statistical significance. The philosophical roots of Hill's viewpoints are unknown. Superficially, they seem to descend from the ideas of Hume and Mill. Hill's viewpoints, however, use a different kind of evidence and have different purposes than do Hume's rules or Mill's system of logic. In a nutshell, Hume ignores comparative evidence central to Hill's viewpoints. Mill's logic disqualifies as invalid nonexperimental evidence, which forms the bulk of epidemiologic findings reviewed from Hill's viewpoints. The approaches by Hume and Mill cannot corroborate successful implementations of Hill's viewpoints. Besides Hume and Mill, the epidemiologic literature is clueless about a plausible, pre-1965 philosophical origin of Hill's viewpoints. Thus, Hill's viewpoints may be philosophically novel, sui generis, still waiting to be validated and justified.

  11. "Great Ideas" in Russian Psychology: Personality Impact on Psychophysiological Functions and Causal Approach to Self- determination

    Directory of Open Access Journals (Sweden)

    Irina A. Mironenko

    2009-01-01

    Full Text Available Russian psychology has brought into the world science at least two great ideas: the conditioned reflex (Pavlov and the zone of proximal development (Vygotsky. These concepts were formulated before “iron curtain” fell. Since then Russian science dropped out from the view of western colleagues for decades. Now it is challenged to re-join international mainstream. Are we in a position to contribute?A key concept for Russian psychology is personality impact on psycho-physiological functions and causal approach to self-determination. The concept of selfdetermination appeared in Western theories in 1980-es and since then it has been developed in the context of teleological humanitarian approach. In Russian science the concept of self-determination dates back to 1934, when it was defined by Rubinstein as “sub’ekt”. Self-determination of ontogenesis of psycho physiological functions resulting from confluence of ontogenesis and social development was explicated by Russian scientists whose theoretical reasoning and empirical results are compared to Western counterparts.

  12. Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

    Science.gov (United States)

    Farhani, Sahbi; Ozturk, Ilhan

    2015-10-01

    The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

  13. Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

    Science.gov (United States)

    Faes, Luca; Nollo, Giandomenico

    2010-11-01

    The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

  14. Strategic planning for MyRA performance: A causal loop diagram approach

    Science.gov (United States)

    Abidin, Norhaslinda Zainal; Zaibidi, Nerda Zura; Karim, Khairah Nazurah

    2017-10-01

    The nexus of research and innovation in higher education are continually receiving worldwide priority attention. Hence, Malaysia has taken its move to enhance public universities as a center of excellence by introducing the status of Research University (RU). To inspire all universities towards becoming a research university, The Ministry of Higher Education (MoHE) had revised an assessment called Malaysian Research Assessment Instrument (MyRA) to evaluate the performance of existence RUs, and other potential higher education institutions. The available spreadsheet tool to access MyRA performance is inadequate to support strategic planning. Since, higher education management is a complex system, in which components and their interactions are ever changing over time, there is a need to for an efficient approach to investigate system behavior and devise research management policies for the benefit of the institution itself and the higher education system. In this paper, we proposed a system dynamics simulation model to evaluate the impact of policies for obtaining the highest performance in MyRA assessment. Causal loop diagram is developed to investigate the relationship of various elements in research management, their inter-relationship that link together and their evolution of behavior over time.

  15. CAUSAL RELATIONSHIP BETWEEN ENERGY CONSUMPTION, ECONOMIC GROWTH AND CO2 EMISSIONS: A DYNAMIC PANEL DATA APPROACH

    Directory of Open Access Journals (Sweden)

    Chaido Dritsaki

    2014-04-01

    Full Text Available Energy plays an important role in economic development worldwide. The increase of energy consumption showed that CO2 emissions in the atmosphere have increased dramatically, and these lead many scientists to push governments of the developing countries to take action for the formulation of environmental policies. Many studies have attempted to look for the direction of causality between energy consumption (EC, economic growth (GDP and CO2 emissions mainly on developing countries. This paper, therefore, applies the panel unit root tests, panel cointegration methods and panel causality test to investigate the relationship between energy consumption (EC, economic growth (GDP and CO2 emissions for three countries of Southern Europe (Greece, Spain, and Portugal covering the annual period 1960-2009. The FMOLS and DOLS are then used to estimate the long run relationship between the variables. The findings of this study reveal that there is a short-run bilateral causal link between the examined variables. However, in the long run, there is a unidirectional causality running from CO2 emissions to energy consumption (EC, and economic growth (GDP and a bilateral causality between energy consumption and economic growth. This indicates that energy is a force for economic growth both in short and long run as it is driven from economic growth. Moreover, to face the heterogeneity on the three countries of Southern Europe we use the FMOLS and DOLS estimation methods.

  16. Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series

    Science.gov (United States)

    Liang, X. S.

    2017-12-01

    Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a "Giant" for the computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming

  17. Disaggregated Energy Consumption and Sectoral Outputs in Thailand: ARDL Bound Testing Approach

    OpenAIRE

    Thurai Murugan Nathan; Venus Khim-Sen Liew; Wing-Keung Wong

    2016-01-01

    From an economic perspective, energy-output relationship studies have become increasingly popular in recent times, partly fuelled by a need to understand the effect of energy on production outputs rather than overall GDP. This study dealt with disaggregated energy consumption and outputs of some major economic sectors in Thailand. ARDL bound testing approach was employed to examine the co-integration relationship. The Granger causality test of the aforementioned ARDL framework was done to inv...

  18. A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks

    NARCIS (Netherlands)

    Blankendaal, Romy; Parinussa, Sarah; Treur, Jan

    2016-01-01

    This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model

  19. Economic growth-electricity consumption causality in 12 European countries: A dynamic panel data approach

    International Nuclear Information System (INIS)

    Ciarreta, A.; Zarraga, A.

    2010-01-01

    This paper applies recent panel methodology to investigate the long-run and causal relationship between electricity consumption and real GDP for a set of 12 European countries using annual data for the period 1970-2007. The sample countries have moved faster than other neighboring countries towards the creation of a single electricity market over the past 30 years. Energy prices are also included in the study due to their important role in affecting the above variables, thus avoiding the problem of omitted variable bias. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a trivariate VECM estimated by system GMM. The results show evidence of a long-run equilibrium relationship between the three series and a negative short-run and strong causality from electricity consumption to GDP. As expected, there is bidirectional causality between energy prices and GDP and weaker evidence between electricity consumption and energy prices. These results support the policies implemented towards the creation of a common European electricity market.

  20. Detecting dynamic causal inference in nonlinear two-phase fracture flow

    Science.gov (United States)

    Faybishenko, Boris

    2017-08-01

    Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

  1. Adolescent Victimization and Early-Adult Psychopathology: Approaching Causal Inference Using a Longitudinal Twin Study to Rule Out Noncausal Explanations

    Science.gov (United States)

    Schaefer, Jonathan D.; Moffitt, Terrie E.; Arseneault, Louise; Danese, Andrea; Fisher, Helen L.; Houts, Renate; Sheridan, Margaret A.; Wertz, Jasmin; Caspi, Avshalom

    2017-01-01

    Adolescence is the peak age for both victimization and mental disorder onset. Previous research has reported associations between victimization exposure and many psychiatric conditions. However, causality remains controversial. Within the Environmental Risk Longitudinal Twin Study, we tested whether seven types of adolescent victimization increased risk of multiple psychiatric conditions and approached causal inference by systematically ruling out noncausal explanations. Longitudinal within-individual analyses showed that victimization was followed by increased mental health problems over a childhood baseline of emotional/behavioral problems. Discordant-twin analyses showed that victimization increased risk of mental health problems independent of family background and genetic risk. Both childhood and adolescent victimization made unique contributions to risk. Victimization predicted heightened generalized liability (the “p factor”) to multiple psychiatric spectra, including internalizing, externalizing, and thought disorders. Results recommend violence reduction and identification and treatment of adolescent victims to reduce psychiatric burden. PMID:29805917

  2. Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test

    International Nuclear Information System (INIS)

    Wesseh, Presley K.; Zoumara, Babette

    2012-01-01

    This contribution investigates causal interdependence between energy consumption and economic growth in Liberia and proposes application of a bootstrap methodology. To better reflect causality, employment is incorporated as additional variable. The study demonstrates evidence of distinct bidirectional Granger causality between energy consumption and economic growth. Additionally, the results show that employment in Liberia Granger causes economic growth and apply irrespective of the short-run or long-run. Evidence from a Monte Carlo experiment reveals that the asymptotic Granger causality test suffers size distortion problem for Liberian data, suggesting that the bootstrap technique employed in this study is more appropriate. Given the empirical results, implications are that energy expansion policies like energy subsidy or low energy tariff for instance, would be necessary to cope with demand exerted as a result of economic growth in Liberia. Furthermore, Liberia might have the performance of its employment generation on the economy partly determined by adequate energy. Therefore, it seems fully justified that a quick shift towards energy production based on clean energy sources may significantly slow down economic growth in Liberia. Hence, the government’s target to implement a long-term strategy to make Liberia a carbon neutral country, and eventually less carbon dependent by 2050 is understandable. - Highlights: ► Causality between energy consumption and economic growth in Liberia investigated. ► There is bidirectional causality between energy consumption and economic growth. ► Energy expansion policies are necessary to cope with demand from economic growth. ► Asymptotic Granger causality test suffers size distortion problem for Liberian data. ► The bootstrap methodology employed in our study is more appropriate.

  3. Calculating the Prior Probability Distribution for a Causal Network Using Maximum Entropy: Alternative Approaches

    Directory of Open Access Journals (Sweden)

    Michael J. Markham

    2011-07-01

    Full Text Available Some problems occurring in Expert Systems can be resolved by employing a causal (Bayesian network and methodologies exist for this purpose. These require data in a specific form and make assumptions about the independence relationships involved. Methodologies using Maximum Entropy (ME are free from these conditions and have the potential to be used in a wider context including systems consisting of given sets of linear and independence constraints, subject to consistency and convergence. ME can also be used to validate results from the causal network methodologies. Three ME methods for determining the prior probability distribution of causal network systems are considered. The first method is Sequential Maximum Entropy in which the computation of a progression of local distributions leads to the over-all distribution. This is followed by development of the Method of Tribus. The development takes the form of an algorithm that includes the handling of explicit independence constraints. These fall into two groups those relating parents of vertices, and those deduced from triangulation of the remaining graph. The third method involves a variation in the part of that algorithm which handles independence constraints. Evidence is presented that this adaptation only requires the linear constraints and the parental independence constraints to emulate the second method in a substantial class of examples.

  4. Economic growth and energy consumption in Algeria: a causality analysis

    International Nuclear Information System (INIS)

    Cherfi, S.

    2011-01-01

    The purpose of this study is to review the causal link in the Granger sense, between energy consumption and economic growth in Algeria, to determine its implications for economic policy. The analysis was done based on Granger static and causality tests using statistical data on per capita primary energy consumption and gross domestic product per inhabitant in Algeria, over the 1965-2008 period. The results of the survey show that there is, in Algeria, a strong link between energy consumption per inhabitant and GDP per inhabitant. The results also suggest the lack of a long term impetus (no co-integration) between energy consumption and economic growth. In addition, there is a one-way causal link between GDP and energy consumption, i.e. the prior GDP data provides a better forecast of energy consumption level, but not the contrary. In other words, GDP explains consumption, not the contrary. (author)

  5. Financial Development and Poverty Reduction Nexus: A Co-Integration and Causality Analysis in Selected Arabic Countries

    Directory of Open Access Journals (Sweden)

    Ayad Hicham

    2017-06-01

    Full Text Available This paper attempts the dynamic causal relationship between poverty reduction measured as consumption per capita and financial development measured as Kaopen and Milesi-Ferreti proxies, trade openness measured measured by the sum of total exports and total imports as a percentage of GDP at 2005 constant prices and economic growth as measured by GDP per capita for 14 selected Arabic countries within the panel co-integration techniques and TYDL Granger causality approach (1996, the results show that the poor people in Arabic countries (the selected countries did not benefit from liberalization systems and economic growth for the period because both of finance-led poverty and trade-led poverty seem to be rejected. The study, therefore, recommends that policy makers should stop the financial braking and adopt new financial policies that allow reducing poverty rates.

  6. Overpressures: Causal Mechanisms, Conventional and Hydromechanical Approaches Surpressions : origine, approches conventionnelle et hydromécanique

    Directory of Open Access Journals (Sweden)

    Grauls D.

    2006-12-01

    Full Text Available Abnormal fluid pressure regimes are commonly encountered at depth in most sedimentary basins. Relationships between effective vertical stress and porosity have been applied, since 1970 to the Gulf Coast area, to assess the magnitude of overpressures. Positive results have been obtained from seismic and basin-modeling techniques in sand-shale, vertical-stress-dominated tertiary basins, whenever compaction disequilibrium conditions apply. However, overpressures resulting from other and/or additional causes (tectonic stress, hydrocarbon generation, thermal stress, fault-related transfer, hydrofracturing. . . cannot be quantitatively assessed using this approach. A hydromechanical approach is then proposed in addition to conventional methods. At any depth, the upper bound fluid pressure is controlled by in situ conditions related to hydrofracturing or fault reactivation. Fluid-driven fracturing implies an episodically open system, under a close to zerominimum effective stress regime. Sound knowledge of present-day tectonic stress regimes allows a direct estimation of minimum stress evolution. A quantitative fluid pressure assessment at depth is therefore possible, as in undrained or/and compartmented geological systems, pressure regimes, whatever their origin, tend to rapidly reach a value close to the minimum principal stress. Therefore, overpressure assessment will be improved, as this methodology can be applied to various geological settings and situations where present-day overpressures originated from other causal mechanisms, very often combined. However, pressure trends in transition zones are more difficult to assess correctly. Additional research on cap rocks and fault seals is therefore required to improve their predictability. In addition to overpressure assessment, the minimum principal stress concept allows a better understanding of petroleum system, as fault-related hydrocarbon dynamic transfers, hydrofractured domains and cap

  7. Corruption, Political Instability and Economic Development in the Economic Community of West African States (ECOWAS: Is There a Causal Relationship?

    Directory of Open Access Journals (Sweden)

    Nurudeen Abu

    2015-04-01

    Full Text Available Despite the abundant research on economic development, corruption and political instability, little research has attempted to examine whether there is a causal relationship among them. This paper examines the causal relationship among corruption, political instability and economic development in the ECOWAS using the Granger causality test within a multivariate cointegration and error-correction framework for the 1996-2012 period. The findings indicate that political instability Granger-causes economic development in the short term, while political instability and economic development Granger-cause corruption in the long term. In addition, we employed the forecast error variance decomposition and impulse response function analyses to investigate the dynamic interaction between the variables. The results demonstrate positive unidirectional Granger causality from political instability to economic development in the short term and positive unidirectional Granger causality from political instability and economic development to corruption in the long term in ECOWAS countries. Thus, ECOWAS governments should employ policies to promote political stability in the region.

  8. DOES GENDER EQUALITY LEAD TO BETTER-PERFORMING ECONOMIES? A BAYESIAN CAUSAL MAP APPROACH

    Directory of Open Access Journals (Sweden)

    Yelda YÜCEL

    2017-01-01

    Full Text Available This study explores the existence of relationships between gender inequalities –represented by the components of the World Economic Forum (WEF Global Gender Gap Index– and the major macroeconomic indicators. The relationships within gender inequalities in education, the labour market, health and the political arena, and between gender inequalities and gross macroeconomic aggregates were modelled with the Bayesian Causal Map, an effective tool that is used to analyze cause-effect relations and conditional dependencies between variables. A data set of 128 countries during the period 2007–2011 is used. Findings reveal that some inequalities have high levels of interaction with each other. In addition, eradicating gender inequalities is found to be associated with better economic performance, mainly in the form of higher gross domestic product growth, investment, and competitiveness.

  9. Causal Analysis After Haavelmo

    Science.gov (United States)

    Heckman, James; Pinto, Rodrigo

    2014-01-01

    Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

  10. CAUSALITY RELATIONSHIP BETWEEN GDP AND ENERGY CONSUMPTION IN GEORGIA, AZERBAIJAN AND ARMENIA

    Directory of Open Access Journals (Sweden)

    Huseyin Kalyoncu

    2013-01-01

    Full Text Available This research aims to investigate the relationship between energy consumption and economic growth in Georgia, Azerbaijan and Armenia during the period of 1995–2009. The Engle-Granger cointegration and Granger causality tests are used in order to analyse the causal relationship between energy consumption and economic growth. It is crucial to see the directions of causality between two variables for the policy makers. For Georgia and Azerbaijan it is found that these two variables are not cointegrated. In case of Armenia these two variables are cointegrated. Accordingly, causality analysis is conducted for Armenia. The research outcomes reveal that there is unidirectional causality from per capita GDP to per capita energy consumption for Armenia.

  11. Linear and nonlinear causal relationship between energy consumption and economic growth in China: New evidence based on wavelet analysis

    Science.gov (United States)

    2018-01-01

    The energy-growth nexus has important policy implications for economic development. The results from many past studies that investigated the causality direction of this nexus can lead to misleading policy guidance. Using data on China from 1953 to 2013, this study shows that an application of causality test on the time series of energy consumption and national output has masked a lot of information. The Toda-Yamamoto test with bootstrapped critical values and the newly proposed non-linear causality test reveal no causal relationship. However, a further application of these tests using series in different time-frequency domain obtained from wavelet decomposition indicates that while energy consumption Granger causes economic growth in the short run, the reverse is true in the medium term. A bidirectional causal relationship is found for the long run. This approach has proven to be superior in unveiling information on the energy-growth nexus that are useful for policy planning over different time horizons. PMID:29782534

  12. Causal universe

    CERN Document Server

    Ellis, George FR; Pabjan, Tadeusz

    2013-01-01

    Written by philosophers, cosmologists, and physicists, this collection of essays deals with causality, which is a core issue for both science and philosophy. Readers will learn about different types of causality in complex systems and about new perspectives on this issue based on physical and cosmological considerations. In addition, the book includes essays pertaining to the problem of causality in ancient Greek philosophy, and to the problem of God's relation to the causal structures of nature viewed in the light of contemporary physics and cosmology.

  13. Performing Causal Configurations in e-Tourism: a Fuzzy-Set Approach

    Directory of Open Access Journals (Sweden)

    Hugues Seraphin

    2016-07-01

    Full Text Available Search engines are constantly endeavouring to integrate social media mentions in the website ranking process. Search Engine Optimization (SEO principles can be used to impact website ranking, considering various social media channels� capability to drive traffic. Both practitioners and researchers has focused on the impact of social media on SEO, but paid little attention to the influences of social media interactions on organic search results. This study explores the causal configurations between social mention variables (strength, sentiment, passion, reach and the rankings of nine websites dedicated to hotel booking (according to organic search results. The social mention variables embedded into the conceptual model were provided by the real-time social media search and analysis tool (www.socialmention.com, while the rankings websites dedicated to hotel booking were determined after a targeted search on Google. The study employs fuzzy-set qualitative comparative analysis (fsQCA and the results reveal that social mention variables has complex links with the rankings of the hotel booking websites included into the sample, according to Quine-McCluskey algorithm solution. The findings extend the body of knowledge related to the impact of social media mentions on

  14. Causal and causally separable processes

    Science.gov (United States)

    Oreshkov, Ognyan; Giarmatzi, Christina

    2016-09-01

    The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

  15. Causal and causally separable processes

    International Nuclear Information System (INIS)

    Oreshkov, Ognyan; Giarmatzi, Christina

    2016-01-01

    The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B , either A is in the causal past of B , B is in the causal past of A , or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B , an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A ’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

  16. On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

    Science.gov (United States)

    Ben Jebli, Mehdi

    2016-08-01

    This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers.

  17. Trivariate causality between economic growth, urbanisation and electricity consumption in Angola: Cointegration and causality analysis

    International Nuclear Information System (INIS)

    Solarin, Sakiru Adebola; Shahbaz, Muhammad

    2013-01-01

    This paper investigates the causal relationship between economic growth, urbanisation and electricity consumption in the case of Angola, while utilizing the data over the period of 1971–2009. We have applied Lee and Strazicich (2003. The Review of Economics and Statistics 63, 1082–1089; 2004. Working Paper. Department of Economics, Appalachian State University) unit root tests to examine the stationarity properties of the series. Using the Gregory–Hansen structural break cointegration procedure as a complement, we employ the ARDL bounds test to investigate long run relationships. The VECM Granger causality test is subsequently used to examine the direction of causality between economic growth, urbanisation, and electricity consumption. Our results indicate the existence of long run relationships. We further observe evidence in favour of bidirectional causality between electricity consumption and economic growth. The feedback hypothesis is also found between urbanisation and economic growth. Urbanisation and electricity consumption Granger cause each other. We conclude that Angola is energy-dependent country. Consequently, the relevant authorities should boost electricity production as one of the means of achieving sustainable economic development in the long run. - Highlights: • We consider the link between electricity consumption and economic growth in Angola. • Urbanisation is added to turn the research into a trivariate investigation. • Various time series procedures are used. • Results show that increasing electricity will improve economic growth in Angola. • Results show urbanisations reduced economic growth during civil war

  18. Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis

    International Nuclear Information System (INIS)

    Jahangir Alam, Mohammad; Ara Begum, Ismat; Buysse, Jeroen; Van Huylenbroeck, Guido

    2012-01-01

    The paper investigates the possible existence of dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth in Bangladesh. First, we have tested cointegration relationships using the Johansen bi-variate cointegration model. This is complemented with an analysis of an auto-regressive distributed lag model to examine the results' robustness. Then, the Granger short-run, the long-run and strong causality are tested with a vector error correction modelling framework. The results indicate that uni-directional causality exists from energy consumption to economic growth both in the short and the long-run while a bi-directional long-run causality exists between electricity consumption and economic growth but no causal relationship exists in short-run. The strong causality results indicate bi-directional causality for both the cases. A uni-directional causality runs from energy consumption to CO 2 emission for the short-run but feedback causality exists in the long-run. CO 2 Granger causes economic growth both in the short and in the long-run. An important policy implication is that energy (electricity as well) can be considered as an important factor for the economic growth in Bangladesh. Moreover, as higher energy consumption also means higher pollution in the long-run, policy makers should stimulate alternative energy sources for meeting up the increasing energy demand. - Highlights: ► Dynamic causality among energy and electricity consumption, CO 2 and economic growth. ► Uni-directional causality exists from energy consumption to economic growth. ► Bi-directional causality exists between electricity consumption and economic growth. ► Feedback causality exists between CO 2 emission to energy consumption. ► CO 2 Granger causes economic growth both in the short and in the long-run.

  19. Estimating Causal Effects of Local Air Pollution on Daily Deaths: Effect of Low Levels.

    Science.gov (United States)

    Schwartz, Joel; Bind, Marie-Abele; Koutrakis, Petros

    2017-01-01

    Although many time-series studies have established associations of daily pollution variations with daily deaths, there are fewer at low concentrations, or focused on locally generated pollution, which is becoming more important as regulations reduce regional transport. Causal modeling approaches are also lacking. We used causal modeling to estimate the impact of local air pollution on mortality at low concentrations. Using an instrumental variable approach, we developed an instrument for variations in local pollution concentrations that is unlikely to be correlated with other causes of death, and examined its association with daily deaths in the Boston, Massachusetts, area. We combined height of the planetary boundary layer and wind speed, which affect concentrations of local emissions, to develop the instrument for particulate matter ≤ 2.5 μm (PM2.5), black carbon (BC), or nitrogen dioxide (NO2) variations that were independent of year, month, and temperature. We also used Granger causality to assess whether omitted variable confounding existed. We estimated that an interquartile range increase in the instrument for local PM2.5 was associated with a 0.90% increase in daily deaths (95% CI: 0.25, 1.56). A similar result was found for BC, and a weaker association with NO2. The Granger test found no evidence of omitted variable confounding for the instrument. A separate test confirmed the instrument was not associated with mortality independent of pollution. Furthermore, the association remained when all days with PM2.5 concentrations > 30 μg/m3 were excluded from the analysis (0.84% increase in daily deaths; 95% CI: 0.19, 1.50). We conclude that there is a causal association of local air pollution with daily deaths at concentrations below U.S. EPA standards. The estimated attributable risk in Boston exceeded 1,800 deaths during the study period, indicating that important public health benefits can follow from further control efforts. Citation: Schwartz J, Bind MA

  20. Lending for Growth? A Granger Causality Analysis of China's Finance-Growth Nexus

    NARCIS (Netherlands)

    Andersson, F.N.G.; Burzynska, K.; Opper, S.

    2016-01-01

    China’s banking sector is dominated by four distinct organizational forms: policy banks (PBs), state-owned commercial banks (SOCBs), joint stock commercial banks (JSCBs), and rural credit cooperatives (RCCs). Economic analyses have especially focused on the development of bank efficiency and

  1. Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

    Czech Academy of Sciences Publication Activity Database

    Čermák, Vladimír; Bodri, L.

    2018-01-01

    Roč. 107, č. 1 (2018), s. 145-152 ISSN 1437-3254 R&D Projects: GA ČR(CZ) GAP210/11/0183; GA MŠk(CZ) LG13040 Institutional support: RVO:67985530 Keywords : geothermics * climate change * ground-air temperature offset * soil temperature * temperature monitoring Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 2.283, year: 2016

  2. Foreign direct investment and policy framework: New Granger causality evidence from African countries

    Directory of Open Access Journals (Sweden)

    Rafiu Adewale Aregbeshola

    2014-11-01

    Full Text Available The strategic importance of foreign direct investment in the contemporary economies has been tremendous.While various countries (developed and developing economies have benefitted from the direct and spillovereffects of FDI, which range from improved technology and knowledge diffusion through to individual andcorporate capability enhancement, FDI outflow remains largely channelled to the developed countries, andthe rapidly developing countries in Asia and South America. Evidence suggests that the developmentenhancingeffects of FDI are felt more highly in the developing economies, such as economies in Africa.However, FDI inflow to the developing economies has been very low. Using data generated from the AfricanDevelopment Indicators (ADI between 1980 and 2008 in econometric estimations, this paper finds thatgovernment policies (especially fiscal and monetary policies play significant roles in facilitating FDI inflow tothe African countries studied. The study thereby suggests an improved regulatory framework to make Africamore attractive to inflow of FDI.

  3. Education and Economic Growth in Pakistan: A Cointegration and Causality Analysis

    Science.gov (United States)

    Afzal, Muhammad; Rehman, Hafeez Ur; Farooq, Muhammad Shahid; Sarwar, Kafeel

    2011-01-01

    This study explored the cointegration and causality between education and economic growth in Pakistan by using time series data on real gross domestic product (RGDP), labour force, physical capital and education from 1970-1971 to 2008-2009 were used. Autoregressive Distributed Lag (ARDL) Model of Cointegration and the Augmented Granger Causality…

  4. Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

    Science.gov (United States)

    Erdem, Ekrem; Tugcu, Can Tansel

    2012-01-01

    This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…

  5. Assessment of Resampling Methods for Causality Testing: A note on the US Inflation Behavior

    NARCIS (Netherlands)

    Papana, A.; Kyrtsou, C.; Kugiumtzis, D.; Diks, C.

    2017-01-01

    Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial

  6. Insurance market penetration and economic growth in Eurozone countries: Time series evidence on causality

    Directory of Open Access Journals (Sweden)

    Saurav Dash

    2018-06-01

    Full Text Available This paper examines the causal relationship between insurance market penetration and per capita economic growth in 19 Eurozone countries for the period 1980–2014. We use three different indicators of insurance market penetration (IMP, namely life insurance penetration, non-life insurance penetration, and total (both life and non-life insurance penetration. We particularly emphasize on whether Granger causality exists between these variables both ways, one way, or not at all. Our empirical results perceive both unidirectional and bidirectional causality between IMP and per capita economic growth. However, these results are mostly non-uniform across the Eurozone countries during this selected period. The policy implication is that the economic policies should recognize the differences in the insurance market and per capita economic growth in order to maintain sustainable growth in the Eurozone. Keywords: IMP, Per capita economic growth, Granger causality, Eurozone countries, JEL codes: L96, O32, O33, O43

  7. Causal Relationship Between Relative Price Variability and Inflation in Turkey:

    Directory of Open Access Journals (Sweden)

    Nebiye Yamak

    2016-09-01

    Full Text Available This study investigates the causal relationship between inflation and relative price variability in Turkey for the period of January 2003-January 2014, by using panel data. In the study, a Granger (1969 non-causality test in heterogeneous panel data models developed by Dumitrescu and Hurlin (2012 is utilized to determine the causal relations between inflation rate relative price variability. The panel data consists of 4123 observations: 133 time observations and 31 cross-section observations. The results of panel causality test indicate that there is a bidirectional causality between inflation rate and relative price variability by not supporting the imperfection information model of Lucas and the menu cost model of Ball and Mankiw.

  8. Causal Relationship between Construction Production and GDP in Turkey

    Directory of Open Access Journals (Sweden)

    Hakkı Kutay Bolkol

    2015-12-01

    Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

  9. Causal Relationship between Construction Production and GDP in Turkey

    Directory of Open Access Journals (Sweden)

    Hakkı Kutay Bolkol

    2015-09-01

    Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

  10. Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

    Czech Academy of Sciences Publication Activity Database

    Krakovská, A.; Jakubík, J.; Chvosteková, M.; Coufal, David; Jajcay, Nikola; Paluš, Milan

    2018-01-01

    Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045 R&D Projects: GA MZd(CZ) NV15-33250A Institutional support: RVO:67985807 Keywords : comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings Impact factor: 2.366, year: 2016 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

  11. On the causal links between FDI and growth in developing countries

    DEFF Research Database (Denmark)

    Hansen, Henrik; Rand, John

    2006-01-01

    We analyse the Granger causal relationships between foreign direct investment (FDI) and GDP in a sample of 31 developing countries covering 31 years. Using estimators for heterogeneous panel data we find bi-directional causality between the FDI-to-GDP ratio and the level of GDP. FDI has a lasting...... of the hypotheses that FDI has an impact on GDP via knowledge transfers and adoption of new technology...

  12. Optimal causal inference: estimating stored information and approximating causal architecture.

    Science.gov (United States)

    Still, Susanne; Crutchfield, James P; Ellison, Christopher J

    2010-09-01

    We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

  13. Causality and headache triggers

    Science.gov (United States)

    Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

    2013-01-01

    Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

  14. Causality tests between stock market development and economic growth in West African Monetary Union

    Directory of Open Access Journals (Sweden)

    Maman Tachiwou ABOUDOU

    2009-12-01

    Full Text Available This paper examines the causal relationship between stock market development and economic growth for the West African Monetary Union economy over the last decade or so. By applying the techniques of unit–root tests and the long–run Granger noncausality test proposed by Toda and Yamamoto (1995, the causal relationships between the real GDP growth rate and two stock market development proxies are tested. The results are in line with the supply leading hypothesis in the sense that there is strong causal flow from the stock market development to economic growth. A unidirectional causal relationship is also observed between real market capitalization ratio and economic growth.

  15. Causal relationship between malocclusion and oral muscles dysfunction: a model of approach.

    Science.gov (United States)

    Saccomanno, S; Antonini, G; D'Alatri, L; D'Angelantonio, M; Fiorita, A; Deli, R

    2012-12-01

    Bad habits result in altered functions which with time can cause anomalies of the orofacial morphology. To solve these problems, orthodontic treatment can be supported by myofunctional therapy in order to recover the normal functionality of the oral muscles. The aim of this study is to assess the need to treat patients with neuromuscular disorders, from both the occlusion and the muscles condition approach in order to obtain the balance needed for the stability of treatment. A sample of 23 patients with atypical swallowing was included in this study, some of them presented thumb sucking and oral breathing. After case history collection, in order to make a correct orthodontic and functional diagnosis, correction of anomalies was carried out since they could compromise the success of the therapy (maxillary contraction, oral breathing, and short lingual fraenum). Then a different therapeutic approach was applied on the basis of the specific dental features. Both from the diagnostic and therapeutic point of view, important results were achieved especially through muscle analysis with dynamometer and surface electromyography. Orthodontic therapy, in the presence of bad habits, is not enough to solve orthodontic issues, it must be combined with a myofunctional treatment. The success of the therapy is granted only when patients and their family comply with the treatment and all factors which can prevent success of the therapy are removed.

  16. Determination of Causality between Remittance and Import: Evidence from Bangladesh

    Directory of Open Access Journals (Sweden)

    Dewan Muktadir-Al-Mukit

    2013-07-01

    Full Text Available This study investigates the relationship between remittance and import for the economy of Bangladesh. The study used different econometric techniques of measuring the long and short term relationship between variables. The Johansen Cointegration test is used to determine the existence of a long term relationships between study variables. The normalized Cointegrating coefficients are found statistically significant and show a stable and positive relationship between study variables. Our Granger causality analysis suggests the existence of a unidirectional causality running from import to remittance. This confirms that remittances have no significant impact on the demand for imported goods rather import exerts a positive shock on the remittance of Bangladesh.

  17. Causality and Information Dynamics in Networked Systems with Many Agents

    Science.gov (United States)

    2017-05-11

    algorithms. 6 Future Perspectives Causal graph reconstruction from noisy data is a problem of central importance. In our research we have shown how the idea ...thrust of the research was to develop methods for GCG sparsification using ideas from Tikhonov regularization and ADMM based proximal algorithms...not vary with time. The notion of Granger-causality is captured in the following definition . Definition 2.1 If ξ̂[xi(t) |Ht] < ξ̂[xi(t) |H−jt ] , (2.2

  18. Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

    2016-09-01

    An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

  19. THE CAUSALITY RELATION BETWEEN FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: AN ANALYSIS FOR EMERGING ECONOMIES

    Directory of Open Access Journals (Sweden)

    Şeref BOZOKLU

    2013-12-01

    Full Text Available This study examines the relationship between financial development and economicgrowth employing panel Granger causality test developed by Dumitrescu ve Hurlin (2012for Brazil, Chile, China, Egypt, Hungry, India, Indonesia, Malaysia, Mexico, Peru,Philippines, South Korea, Thailand and Turkey. We used yearly data over the period 1988-2011. Domestic credits to Gross Domestic Product (GDP ratio and real GDP per capitaare used as indicators for financial development and economic growth respectively. Theempirical results strongly indicate that financial development Granger-causes economicgrowth and that these countries can accelerate their growth rates by improving theirfinancial systems. 

  20. Explaining Racial Disparities in Child Asthma Readmission Using a Causal Inference Approach.

    Science.gov (United States)

    Beck, Andrew F; Huang, Bin; Auger, Katherine A; Ryan, Patrick H; Chen, Chen; Kahn, Robert S

    2016-07-01

    difference between African American and white children with respect to readmission hazard no longer reached the level of significance (hazard ratio, 1.18; 95% CI, 0.87-1.60; Cox P = .30 and log-rank P = .39). A total of 80% of the observed readmission disparity between African American and white children could be explained after statistically balancing available biologic, environmental, disease management, access to care, and socioeconomic and hardship variables across racial groups. Such a comprehensive, well-framed approach to exposures that are associated with morbidity is critical as we attempt to better understand and lessen persistent child asthma disparities.

  1. Impact of environmental inputs on reverse-engineering approach to network structures.

    Science.gov (United States)

    Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

    2009-12-04

    Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

  2. A broadened causality in variance approach to assess the risk dynamics between crude oil prices and the Jordanian stock market

    International Nuclear Information System (INIS)

    Bouri, Elie

    2015-01-01

    Within the new developed causality-in-variance approach, this paper builds up a broad methodological framework to more accurately capture the risk spillover effects between global oil prices and Jordanian stock market returns during the period 1 March 2003–31 January 2014. The sample period is divided, on the basis of the 2008 financial crisis, into pre-crisis and post-crisis periods. Results for the pre-crisis period show a lack of risk spillovers between global oil and the Jordanian stock market. After the crisis, however, we find evidence for one-way risk spillover running from the oil market. These findings have implications for the design of appropriate asset allocation and regulatory policies to manage risk spillover effects. -- Highlights: •A broad methodological framework accurately seizes dynamic risk spillover between oil prices and Jordanian stock returns. •We find insignificant risk spillover until the start of the financial crisis. •Crude oil transmits its risk to the Jordanian stock market

  3. Attribution and social cognitive neuroscience: a new approach for the "online-assessment" of causality ascriptions and their emotional consequences.

    Science.gov (United States)

    Terbeck, Sylvia; Chesterman, Paul; Fischmeister, Florian Ph S; Leodolter, Ulrich; Bauer, Herbert

    2008-08-15

    Attribution theory plays a central role in understanding cognitive processes that have emotional consequences; however, there has been very limited attention to its neural basis. After reviewing classical studies in social psychology in which attribution has been experimentally manipulated we developed a new approach that allows the investigation of state attributions and emotional consequences using neuroscience methodologies. Participants responded to the Erikson Flanker Task, but, in order to maintain the participant's beliefs about the nature of the task and to produce a significant number of error responses, an adaptive algorithm tuned the available time to respond such that, dependent on the subject's current performance, the negative feedback rate was held at chance level. In order to initiate variation in attribution participants were informed that one and the same task was either easy or difficult. As a result of these two different instructions the two groups differed significantly in error attribution only on the locus of causality dimension. Additionally, attributions were found to be stable over a large number of trials, while accuracy and reaction time remained the same. Thus, the new paradigm is particularly suitable for cognitive neuroscience research that evaluates brain behaviour relationships of higher order processes in 'simulated achievement settings'.

  4. The causal effect of red blood cell folate on genome-wide methylation in cord blood: a Mendelian randomization approach.

    Science.gov (United States)

    Binder, Alexandra M; Michels, Karin B

    2013-12-04

    Investigation of the biological mechanism by which folate acts to affect fetal development can inform appraisal of expected benefits and risk management. This research is ethically imperative given the ubiquity of folic acid fortified products in the US. Considering that folate is an essential component in the one-carbon metabolism pathway that provides methyl groups for DNA methylation, epigenetic modifications provide a putative molecular mechanism mediating the effect of folic acid supplementation on neonatal and pediatric outcomes. In this study we use a Mendelian Randomization Unnecessary approach to assess the effect of red blood cell (RBC) folate on genome-wide DNA methylation in cord blood. Site-specific CpG methylation within the proximal promoter regions of approximately 14,500 genes was analyzed using the Illumina Infinium Human Methylation27 Bead Chip for 50 infants from the Epigenetic Birth Cohort at Brigham and Women's Hospital in Boston. Using methylenetetrahydrofolate reductase genotype as the instrument, the Mendelian Randomization approach identified 7 CpG loci with a significant (mostly positive) association between RBC folate and methylation level. Among the genes in closest proximity to this significant subset of CpG loci, several enriched biologic processes were involved in nucleic acid transport and metabolic processing. Compared to the standard ordinary least squares regression method, our estimates were demonstrated to be more robust to unmeasured confounding. To the authors' knowledge, this is the largest genome-wide analysis of the effects of folate on methylation pattern, and the first to employ Mendelian Randomization to assess the effects of an exposure on epigenetic modifications. These results can help guide future analyses of the causal effects of periconceptional folate levels on candidate pathways.

  5. Causal inference in public health.

    Science.gov (United States)

    Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M

    2013-01-01

    Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.

  6. Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

    Science.gov (United States)

    Duggento, A; Passamonti, L; Guerrisi, M; Toschi, N

    2017-07-01

    In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

  7. Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan

    International Nuclear Information System (INIS)

    Yang, Cheng-Lang; Lin, Hung-Pin; Chang, Chih-Heng

    2010-01-01

    This study investigates the linear and nonlinear causality between the total electricity consumption (TEC) and real gross domestic production (RGDP). Unlike previous literature, we solve the undetermined relation between RGDP and electricity consumption by classifying TEC into industrial sector consumption (ISC) and residential sector consumption (RSC) as well as investigating how TEC, ISC, and RSC influence Taiwan's RGDP. By using the Granger's linear causality test, it is shown that (i) there is a bidirectional causality among TEC, ISC, and RGDP, but a neutrality between RSC and RGDP with regard to the linear causality and (ii) there is still a bidirectional causality between TEC and RGDP, but a unidirectional causality between RSC and RGDP with regard to the nonlinear causality. On the basis of (i) and (ii), we suggest that the electricity policy formulators loosen the restriction on ISC and limit RSC in order to achieve the goal of economic growth.

  8. Energy, human capital and economic growth in Asia Pacific countries — Evidence from a panel cointegration and causality analysis

    International Nuclear Information System (INIS)

    Fang, Zheng; Chang, Youngho

    2016-01-01

    This paper examines the cointegration and causal relationship between energy consumption and economic development in 16 Asia Pacific countries over the period 1970–2011 using the augmented production function which considers not only physical capital and labor but also human capital. This is likely among the first of the energy–growth nexus literature to include human capital in the multivariate framework. Using recently developed panel unit root test and cointegration test that allow for cross-sectional dependence, this paper finds a long-run cointegrating relationship between these variables. Continuously-updated fully modified (Cup-FM) estimates are subsequently compared with panel heterogeneous fully modified ordinary least squares (FMOLS) results to confirm the importance of accounting for interdependence across countries. The bootstrap panel Granger causality test results find economic growth Granger cause energy use in the region but the relationship varies for individual countries. - Highlights: • We study the causal link between energy and growth in 16 AP countries for 1970–2011. • Human capital is for the first time incorporated into the multivariate framework. • Recent panel methods allowing for cross sectional dependence is used. • Bootstrap panel Granger causality test results find GDP Granger causing energy use in the region. • The energy–growth relationship varies for individual countries.

  9. Causality and cointegration analysis between macroeconomic variables and the Bovespa.

    Directory of Open Access Journals (Sweden)

    Fabiano Mello da Silva

    Full Text Available The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI, industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa. The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988 causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988 long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

  10. Causality and cointegration analysis between macroeconomic variables and the Bovespa.

    Science.gov (United States)

    da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

    2014-01-01

    The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

  11. Causal inference based on counterfactuals

    Directory of Open Access Journals (Sweden)

    Höfler M

    2005-09-01

    Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

  12. The causal nexus between oil prices and equity market in the U.S.: A regime switching model

    International Nuclear Information System (INIS)

    Balcilar, Mehmet; Ozdemir, Zeynel Abidin

    2013-01-01

    The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S and P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S and P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S and P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S and P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns. - Highlights: • We analyse the causal links between oil futures price and a sub-grouping of S and P 500 index. • The causal links are modelled using a regime switching model. • We do not find any lead–lag type Granger causality between the series. • The results show that oil futures price has regime prediction power for a sub-grouping of S and P 500 stock index

  13. The Bidirectional Causality between Country-Level Governance, Economic Growth and Sustainable Development: A Cross-Country Data Analysis

    Directory of Open Access Journals (Sweden)

    Cristina Boţa-Avram

    2018-02-01

    Full Text Available In the context of contemporary society, characterized by the information users’ growing and differentiated needs, the way country-level governance and social responsibility contribute to the ensuring of sustainable economic development is a concern for all the actors of the economic sphere. The aim of this paper is to test the causal linkages between the quality of country-level governance, economic growth and a well-known indicator of economic sustainable development, for a large panel of world-wide countries for a period of 10 years (2006–2015. While there are some prior studies that have argued the bidirectional causality between good public governance and economic development, this study intends to provide a new focus on the relationship between country-level governance and economic growth, on one hand, and between country-level governance and adjusted net savings, as a selected indicator of economic sustainable development, on the other hand. Four hypotheses on the causal relationship between good governance, economic growth and sustainable development were tested by using Granger non-causality tests. Our findings resulting from Granger non-causality tests provide reasonable evidence of Granger causality from country-level governance to economic growth, but from economic growth to country-level governance, the causality is not confirmed. In what regards the relationship between country-level governance and adjusted net savings, the bidirectional Granger causality is not confirmed. The main implication of our study is that improving economic growth and sustainable development is a very challenging issue, and the impact of macro-level factors such as country-level governance should not be neglected.

  14. The topology of a causal network for the Chinese financial system

    Science.gov (United States)

    Gao, Bo; Ren, Ruo-en

    2013-07-01

    The paper builds a causal network for the Chinese financial system based on the Granger causality of company risks, studies its different topologies in crisis and bull period, and applies the centrality to explain individual risk and prevent systemic risk. The results show that this causal network possesses both small-world phenomenon and scale-free property, and has a little different average distance, clustering coefficient, and degree distribution in different periods, and financial institutions with high centrality not only have large individual risk, but also are important for systemic risk immunization.

  15. Nuclear energy consumption, oil consumption and economic growth in G-6 countries: Bootstrap panel causality test

    International Nuclear Information System (INIS)

    Chu, Hsiao-Ping; Chang Tsangyao

    2012-01-01

    This study applies bootstrap panel Granger causality to test whether energy consumption promotes economic growth using data from G-6 countries over the period of 1971–2010. Both nuclear and oil consumption data are used in this study. Regarding the nuclear consumption-economic growth nexus, nuclear consumption causes economic growth in Japan, the UK, and the US; economic growth causes nuclear consumption in the US; nuclear consumption and economic growth show no causal relation in Canada, France and Germany. Regarding oil consumption-economic growth nexus, we find that there is one-way causality from economic growth to oil consumption only in the US, and that oil consumption does not Granger cause economic growth in G-6 countries except Germany and Japan. Our results have important policy implications for the G-6 countries within the context of economic development. - Highlights: ► Bootstrap panel Granger causality test whether energy consumption promotes economic growth. ► Data from G-6 countries for both nuclear and oil consumption data are used. ► Results have important policy implications within the context of economic development.

  16. Exploring the relationship between child physical abuse and adult dating violence using a causal inference approach in an emerging adult population in South Korea.

    Science.gov (United States)

    Jennings, Wesley G; Park, MiRang; Richards, Tara N; Tomsich, Elizabeth; Gover, Angela; Powers, Ráchael A

    2014-12-01

    Child maltreatment is one of the most commonly examined risk factors for violence in dating relationships. Often referred to as the intergenerational transmission of violence or cycle of violence, a fair amount of research suggests that experiencing abuse during childhood significantly increases the likelihood of involvement in violent relationships later, but these conclusions are primarily based on correlational research designs. Furthermore, the majority of research linking childhood maltreatment and dating violence has focused on samples of young people from the United States. Considering these limitations, the current study uses a rigorous, propensity score matching approach to estimate the causal effect of experiencing child physical abuse on adult dating violence among a large sample of South Korean emerging adults. Results indicate that the link between child physical abuse and adult dating violence is spurious rather than causal. Study limitations and implications are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Capturing connectivity and causality in complex industrial processes

    CERN Document Server

    Yang, Fan; Shah, Sirish L; Chen, Tongwen

    2014-01-01

    This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways: ·      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and ·      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian ne...

  18. A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.

    Science.gov (United States)

    Balzer, Laura B; Zheng, Wenjing; van der Laan, Mark J; Petersen, Maya L

    2018-01-01

    We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual's covariates on another's outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment.

  19. Spectral dimension in causal set quantum gravity

    International Nuclear Information System (INIS)

    Eichhorn, Astrid; Mizera, Sebastian

    2014-01-01

    We evaluate the spectral dimension in causal set quantum gravity by simulating random walks on causal sets. In contrast to other approaches to quantum gravity, we find an increasing spectral dimension at small scales. This observation can be connected to the nonlocality of causal set theory that is deeply rooted in its fundamentally Lorentzian nature. Based on its large-scale behaviour, we conjecture that the spectral dimension can serve as a tool to distinguish causal sets that approximate manifolds from those that do not. As a new tool to probe quantum spacetime in different quantum gravity approaches, we introduce a novel dimensional estimator, the causal spectral dimension, based on the meeting probability of two random walkers, which respect the causal structure of the quantum spacetime. We discuss a causal-set example, where the spectral dimension and the causal spectral dimension differ, due to the existence of a preferred foliation. (paper)

  20. Causal Set Generator and Action Computer

    OpenAIRE

    Cunningham, William; Krioukov, Dmitri

    2017-01-01

    The causal set approach to quantum gravity has gained traction over the past three decades, but numerical experiments involving causal sets have been limited to relatively small scales. The software suite presented here provides a new framework for the generation and study of causal sets. Its efficiency surpasses previous implementations by several orders of magnitude. We highlight several important features of the code, including the compact data structures, the $O(N^2)$ causal set generatio...

  1. Investigating the effect of external trauma through a dynamic system modeling approach for clustering causality in diabetic foot ulcer development.

    Science.gov (United States)

    Salimi, Parisa; Hamedi, Mohsen; Jamshidi, Nima; Vismeh, Milad

    2017-04-01

    Diabetes and its associated complications are realized as one of the most challenging medical conditions threatening more than 29 million people only in the USA. The forecasts suggest a suffering of more than half a billion worldwide by 2030. Amid all diabetic complications, diabetic foot ulcer (DFU) has attracted much scientific investigations to lead to a better management of this disease. In this paper, a system thinking methodology is adopted to investigate the dynamic nature of the ulceration. The causal loop diagram as a tool is utilized to illustrate the well-researched relations and interrelations between causes of the DFU. The result of clustering causality evaluation suggests a vicious loop that relates external trauma to callus. Consequently a hypothesis is presented which localizes development of foot ulceration considering distribution of normal and shear stress. It specifies that normal and tangential forces, as the main representatives of external trauma, play the most important role in foot ulceration. The evaluation of this hypothesis suggests the significance of the information related to both normal and shear stress for managing DFU. The results also discusses how these two react on different locations on foot such as metatarsal head, heel and hallux. The findings of this study can facilitate tackling the complexity of DFU problem and looking for constructive mitigation measures. Moreover they lead to developing a more promising methodology for managing DFU including better prognosis, designing prosthesis and insoles for DFU and patient caring recommendations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

    Directory of Open Access Journals (Sweden)

    Ibrahim Delibalta

    2017-01-01

    Full Text Available We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.

  3. Discrete causal theory emergent spacetime and the causal metric hypothesis

    CERN Document Server

    Dribus, Benjamin F

    2017-01-01

    This book evaluates and suggests potentially critical improvements to causal set theory, one of the best-motivated approaches to the outstanding problems of fundamental physics. Spacetime structure is of central importance to physics beyond general relativity and the standard model. The causal metric hypothesis treats causal relations as the basis of this structure. The book develops the consequences of this hypothesis under the assumption of a fundamental scale, with smooth spacetime geometry viewed as emergent. This approach resembles causal set theory, but differs in important ways; for example, the relative viewpoint, emphasizing relations between pairs of events, and relationships between pairs of histories, is central. The book culminates in a dynamical law for quantum spacetime, derived via generalized path summation.

  4. Sectoral analysis of the causal relationship between electricity consumption and real output in Pakistan

    International Nuclear Information System (INIS)

    Tang, Chor Foon; Shahbaz, Muhammad

    2013-01-01

    This study uses the annual data from 1972 to 2010 to assess the causal relationship between electricity consumption and real output at the aggregate and sectoral levels in Pakistan. This study covers three main economic sectors in Pakistan namely agricultural, manufacturing and services sectors. Our cointegration results reveal that the variables are cointegrated at the aggregate and sectoral levels. At the aggregate level, we find that there is uni-directional Granger causality running from electricity consumption to real output in Pakistan. At the sectoral level, we find that electricity consumption Granger-causes real output in the manufacturing and services sectors. However, there is no causal relationship between electricity consumption and real output in the agricultural sector. The policy implication of these results is that electricity conservation policies in general would deteriorate the process of economic growth as well as the real output in the manufacturing and services sectors in Pakistan. Nevertheless, we suggest the Pakistani government to implement the electricity conservation policies merely to the agricultural sector because such policies may have less or no adverse impact on its real output. - Highlights: • We assess the electricity-growth nexus in Pakistan at the aggregate and sectoral levels. • The variables are cointegrated at both levels. • We find causality from electricity to output at the aggregate level and services. • We find neutral causality in the agricultural sector. • We find bi-directional causality in the manufacturing sector

  5. Energy and economic growth in the USA: a multivariate approach

    International Nuclear Information System (INIS)

    Stern, D.I.

    1993-01-01

    This paper examines the casual relationship between Gross Domestic Product and energy use for the period 1947-90 in the United States of America. The relationship between energy use and economic growth has been examined by both biophysical and neoclassical economists. In particular, several studies have tested for the presence of a causal relationships (in the Granger sense) between energy use and economic growth. However, these tests do not allow a direct test of the relative explanatory powers of the neoclassical and biophysical models. A multivariate adaptation of the test-vector autoregression (VAR) does allow such a test. A VAR of GDP, energy use, capital stock and employment is estimated and Granger tests for causal relationships between the variables are carried out. Although there is no evidence that gross energy use Granger causes GDP, a measure of final energy use adjusted for changing fuel composition does Granger cause GDP. (author)

  6. Causal Relationship Between Relative Price Variability and Inflation in Turkey: Evidence from Panel Data

    Directory of Open Access Journals (Sweden)

    Nebiye Yamak

    2016-06-01

    Full Text Available This study investigates the causal relationship between inflation and relative price variability in Turkey for the period of January 2003-January 2014, by using panel data. In the study, a Granger (1969 non-causality test in heterogeneous panel data models developed by Dumitrescu and Hurlin (2012 is utilized to determine the causal relations between inflation rate relative price variability. The panel data consists of 4123 observations: 133 time observations and 31 cross-section observations. The results of panel causality test indicate that there is a bidirectional causality between inflation rate and relative price variability by not supporting the imperfection information model of Lucas and the menu cost model of Ball and Mankiw.

  7. The direction of causality between exports and firm performance: microeconomic evidence from Croatia using the matching approach

    Directory of Open Access Journals (Sweden)

    Miljana Valdec

    2015-03-01

    Full Text Available This paper contributes to the literature by using propensity score matching to test for causal effects of starting to export on firm performance in Croatian manufacturing firm-level data. The results confirm that exporters have characteristics superior to those of non-exporters. In the main sample specification there is pervasive evidence of self-selection into export markets, meaning that firms are successful years before they become exporters. Using multiple firm performance indicators, panel and cross section data models together with various sample specifications there is scant evidence on learning-by-exporting which holds true only in a few cases. On the other hand, higher sales growth is found to be a more conclusive distinguishing characteristic of new exporters. As in similar studies, we find that a part of the results depends on the number of export starters in the estimation sample.

  8. Inference of directed climate networks: role of instability of causality estimation methods

    Science.gov (United States)

    Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

    2013-04-01

    Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

  9. Causally nonseparable processes admitting a causal model

    International Nuclear Information System (INIS)

    Feix, Adrien; Araújo, Mateus; Brukner, Caslav

    2016-01-01

    A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities ) while others do not (they admit a ‘causal model’ analogous to a local model ). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties. (paper)

  10. Causas do desmatamento da Amazônia: uma aplicação do teste de causalidade de Granger acerca das principais fontes de desmatamento nos municípios da Amazônia Legal brasileira

    Directory of Open Access Journals (Sweden)

    Marcelo Bentes Diniz

    2009-04-01

    Full Text Available Many are the factors indicated by the pertinent literature concerning the causes of deforestation in Brazilian Legal Amazon. From endogenous aspects as the edafo-climatic conditions to aspects related to anthropic action, like the population movements, urban growth, and especially, the independent or induced actions of the different public and private economic agents who have acted in the region, historically configuring the processes of occupation of the land and economic exploitation of the Amazonian region. The objective of this article is to perform a causality test, in the Granger sense, in the main variables suggested as important that explain the deforestation of the Legal Amazon, in the period from 1997 to 2006. The methodology to be used is based on dynamic models for the panel data, developed by Holtz-Eakin et al. (1988 and Arellano-Bond (1991 who developed a causality test based on the seminal article of Granger (1969. Among the main results found is the empirical evidence that there is a bidirectional causality between deforestation and the areas of permanent and temporary cultures, as well as the size of the cattle herd.

  11. An Integrated Approach to Explore the Relationship Among Economic, Construction Land Use, and Ecology Subsystems in Zhejiang Province, China

    Directory of Open Access Journals (Sweden)

    Chuyu Xia

    2016-05-01

    Full Text Available Zhejiang Province, China is experiencing rapid urbanization, facing the challenge of coupling socioeconomic development and ecological conservation. This paper establishes a comprehensive index system to assess coordinating development of economic, construction land use (CLU, and ecology subsystems. A Granger test and a coupling coordination model were applied to explore the causal relationship and the coordinated development state among the three subsystems from 2000 to 2012. The results showed that: (1 changes in the integrated value of the economic subsystem were the Granger cause of changes in the ecology and CLU subsystems, and the changes in the integrated values of ecology and CLU was each other’s Granger cause; (2 the coupling coordination relationship of the integrated value for economic–CLU–ecology was constrained by the relationship between the economic and the CLU subsystems from 2000 to 2004, and that between the ecology and the economic subsystems was the impediment of the sustainable development of economic–CLU–ecology from 2004 to 2012. This research helps to identify approach to sustainable development through analyzing synergistic effects, interdependencies, and trade-offs among the integrated economic–CLU–ecology values, and to make significant contribution to urban planning policies in rapid urbanization region.

  12. Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS

    International Nuclear Information System (INIS)

    Keppler, Jan Horst; Mansanet-Bataller, Maria

    2010-01-01

    The topic of this article is the analysis of the interplay between daily carbon, electricity and gas price data with the European Union Emission Trading System (EU ETS) for CO 2 emissions. In a first step we have performed Granger causality tests for Phase I of the EU ETS (January 2005 until December 2007) and the first year of Phase II of the EU ETS (2008). The analysis includes both spot and forward markets - given the close interactions between the two sets of markets. The results show that during Phase I coal and gas prices, through the clean dark and spark spread, impacted CO 2 futures prices, which in return Granger caused electricity prices. During the first year of the Phase II, the short-run rent capture theory (in which electricity prices Granger cause CO 2 prices) prevailed. On the basis of the qualitative results of the Granger causality tests we obtained the formulation testable equations for quantitative analysis. Standard OLS regressions yielded statistically robust and theoretically coherent results. (author)

  13. Automated service quality and its behavioural consequences in CRM Environment: A structural equation modeling and causal loop diagramming approach

    Directory of Open Access Journals (Sweden)

    Arup Kumar Baksi

    2012-08-01

    Full Text Available Information technology induced communications (ICTs have revolutionized the operational aspects of service sector and have triggered a perceptual shift in service quality as rapid dis-intermediation has changed the access-mode of services on part of the consumers. ICT-enabled services further stimulated the perception of automated service quality with renewed dimensions and there subsequent significance to influence the behavioural outcomes of the consumers. Customer Relationship Management (CRM has emerged as an offshoot to technological breakthrough as it ensured service-encapsulation by integrating people, process and technology. This paper attempts to explore the relationship between automated service quality and its behavioural consequences in a relatively novel business-philosophy – CRM. The study has been conducted on the largest public sector bank of India - State bank of India (SBI at Kolkata which has successfully completed its decade-long operational automation in the year 2008. The study used structural equation modeling (SEM to justify the proposed model construct and causal loop diagramming (CLD to depict the negative and positive linkages between the variables.

  14. Le concept d' histoire dans la philosophie de Gilles-Gaston Granger.

    Directory of Open Access Journals (Sweden)

    Philippe Lacour

    2004-03-01

    éalisations cognitives. En conclusion, il suggère que le débat historiographique contemporain, portant sur les prétentions de l'histoire à la vérité, tourne précisément autour de ces deux difficultés (l'individuel, le récit, qui ne sont en fait que les deux aspects d'un même problème : comment le discours historique peut-il atteindre son objet individuel (défini a minima comme événement temporel ? D'où l'intérêt archéologique de la présente enquête. Throughout his career, Granger developed a rigorous reflection on the sciences, within the French tradition of applied rationalism. Yet his approach was highly comparative, while its framework was both pragmatist and transcendental. Nevertheless, his formal conception of knowledge provided history with a paradoxical status. The first part of this article attempts to qualify this “paradox” of historical knowledge through the study of five constituents of the discipline that take it apart from holy scientificity: its direct link to experience, its focus on the individual, its “weak” verification, its representative rather than objectivist goal, its lack of virtualization. The second part consists in a criticism of Granger’s way of posing the classic problem of the ambiguity of historical knowledge. The focus is on Granger’s treatment of the focus on the individual as well as on his failure to fully consider the role of the narrative or historical discourse. The need to take into account the linguistic specificity and cognitive realizations of historical discourse within the epistemological reflection is thus highlighted. The conclusion suggests that the contemporary debate within historiography on the pretentions of history toward truth is precisely around these two difficulties (the individual and the narrative, in fact both aspects of a single problem: how can historical discourse reach its individual object (i.e., the temporal event? Hence the archeological value of the present investigation.

  15. Bayesian networks improve causal environmental ...

    Science.gov (United States)

    Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

  16. Campbell's and Rubin's Perspectives on Causal Inference

    Science.gov (United States)

    West, Stephen G.; Thoemmes, Felix

    2010-01-01

    Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

  17. The causality between energy consumption and economic growth in Turkey

    International Nuclear Information System (INIS)

    Erdal, Guelistan; Erdal, Hilmi; Esenguen, Kemal

    2008-01-01

    This paper applies the causality test to examine the causal relationship between primary energy consumption (EC) and real Gross National Product (GNP) for Turkey during 1970-2006. We employ unit root tests, the augmented Dickey-Fuller (ADF) and the Philips-Perron (PP), Johansen cointegration test, and Pair-wise Granger causality test to examine relation between EC and GNP. Our empirical results indicate that the two series are found to be non-stationary. However, first differences of these series lead to stationarity. Further, the results indicate that EC and GNP are cointegrated and there is bidirectional causality running from EC to GNP and vice versa. This means that an increase in EC directly affects economic growth and that economic growth also stimulates further EC. This bidirectional causality relationship between EC and GNP determined for Turkey at 1970-2006 period is in accordance with the ones in literature reported for similar countries. Consequently, we conclude that energy is a limiting factor to economic growth in Turkey and, hence, shocks to energy supply will have a negative impact on economic growth

  18. Causality analysis of diesel consumption and economic growth in Cameroon

    International Nuclear Information System (INIS)

    Tamba, Jean Gaston; Njomo, Donatien; Limanond, Thirayoot; Ntsafack, Borel

    2012-01-01

    This study examines the causal relationship between diesel consumption and economic growth in Cameroon by using a three-step modern time-series technique. Tests for unit roots, cointegration, and Granger-causality based on error correction model are employed on annual data covering the period 1975–2008. Empirical results of the study confirm the presence of a long-run equilibrium relationship between diesel consumption and economic growth. The error correction model shows that an estimated 1% increase in economic growth causes a rise in diesel consumption of 1.30% in the long-run. The overall results show that there exists bidirectional causality in the long-run relationship and no causality in the short-run relationship between diesel consumption and economic growth at the 5% level of significance. Thus, the energy policies in Cameroon should place priority on the discovery of new oil field and building capacity additions of the refinery to increase production of petroleum products, as this would propel the economic growth of the country. - Highlights: ► We examine the causal relationship between diesel consumption and GDP in Cameroon. ► we analyze the petroleum products sector in Cameroon. ► 1% increase in economic growth causes a rise in diesel consumption of 1.30%. ► The policy aimed at improving diesel supply have a positive impact on economics.

  19. On the Temporal Causal Relationship Between Macroeconomic Variables

    Directory of Open Access Journals (Sweden)

    Srinivasan Palamalai

    2014-02-01

    Full Text Available The present study examines the dynamic interactions among macroeconomic variables such as real output, prices, money supply, interest rate (IR, and exchange rate (EXR in India during the pre-economic crisis and economic crisis periods, using the autoregressive distributed lag (ARDL bounds test for cointegration, Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on Vector Error Correction Model, variance decomposition analysis and impulse response functions. The empirical results reveal a stronger long-run bilateral relationship between real output, price level, IR, and EXR during the pre-crisis sample period. Moreover, the empirical results confirm a unidirectional short-run causality running from price level to EXR, IR to price level, and real output to money supply during the pre-crisis period. Also, it is evident from the test results that there exist short-run bidirectional relationships running between real output and EXR, price level and IR, and IR and EXR in the pre-crisis era, respectively. Most importantly, long-run bidirectional causality is found between real output, EXR, and IR during the economic crisis period. And the study results indicate short-run bidirectional causality between money supply and EXR, IR and price level, and IR and output in India during the crisis era. Also, a short-run unidirectional causality runs from prices to real output in the crisis period.

  20. Causality between public policies and exports of renewable energy technologies

    International Nuclear Information System (INIS)

    Sung, Bongsuk; Song, Woo-Yong

    2013-01-01

    This article investigates the causal relationship between public policies and exports of renewable energy technologies using panel data from 18 countries for the period 1991–2007. A number of panel unit root and cointegration tests are applied. Time series data on public policies and exports are integrated and cointegrated. The dynamic OLS results indicate that in the long run, a 1% increase in government R and D expenditures (RAD) increases exports (EX) by 0.819%. EX and RAD variables respond to deviations from the long-run equilibrium in the previous period. Additionally, the Blundell–Bond system generalized methods of moments (GMM) is employed to conduct a panel causality test in a vector error-correction mechanism (VECM) setting. Evidence of a bidirectional and short-run, and strong causal relationship between EX and the contribution of renewable energy to the total energy supply (CRES) is uncovered. CRES has a negative effect on EX, whereas EX has a positive effect on CRES. We suggest some policy implications based on the results of this study. - Highlights: ► We model VECM to test the Granger causality between the policies and the export. ► Technology-push policy has a positive impact on export in the long-run. ► There are the short-run causal relationships between market-pull policy and export

  1. Causal reasoning in physics

    CERN Document Server

    Frisch, Mathias

    2014-01-01

    Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science.

  2. Theories of Causality

    Science.gov (United States)

    Jones, Robert

    2010-03-01

    There are a wide range of views on causality. To some (e.g. Karl Popper) causality is superfluous. Bertrand Russell said ``In advanced science the word cause never occurs. Causality is a relic of a bygone age.'' At the other extreme Rafael Sorkin and L. Bombelli suggest that space and time do not exist but are only an approximation to a reality that is simply a discrete ordered set, a ``causal set.'' For them causality IS reality. Others, like Judea Pearl and Nancy Cartwright are seaking to build a complex fundamental theory of causality (Causality, Cambridge Univ. Press, 2000) Or perhaps a theory of causality is simply the theory of functions. This is more or less my take on causality.

  3. Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form

    DEFF Research Database (Denmark)

    Péguin-Feissolle, Anne; Strikholm, Birgit; Teräsvirta, Timo

    In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests b...

  4. Causality in Science

    Directory of Open Access Journals (Sweden)

    Cristina Puente Águeda

    2011-10-01

    Full Text Available Causality is a fundamental notion in every field of science. Since the times of Aristotle, causal relationships have been a matter of study as a way to generate knowledge and provide for explanations. In this paper I review the notion of causality through different scientific areas such as physics, biology, engineering, etc. In the scientific area, causality is usually seen as a precise relation: the same cause provokes always the same effect. But in the everyday world, the links between cause and effect are frequently imprecise or imperfect in nature. Fuzzy logic offers an adequate framework for dealing with imperfect causality, so a few notions of fuzzy causality are introduced.

  5. Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

    Science.gov (United States)

    Levine, Matthew E; Albers, David J; Hripcsak, George

    2016-01-01

    Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

  6. Canonical correlation analysis and Wiener-Granger causality tests : Useful tools for the specification of VAR models

    NARCIS (Netherlands)

    Horvath, C.; Leeflang, P.S.H.; Otter, P.W.

    Dynamic multivariate models ha e become popular in analyzing the behavior of competitive marketing systems because they are capable of incorporating all the relationships in a competitive marketing environment. In this paper we consider VAR models, the most frequently used dynamic multivariate

  7. Laterality of Temporoparietal Causal Connectivity during the Prestimulus Period Correlates with Phonological Decoding Task Performance in Dyslexic and Typical Readers

    OpenAIRE

    Frye, Richard E.; Liederman, Jacqueline; McGraw Fisher, Janet; Wu, Meng-Hung

    2011-01-01

    We examined how effective connectivity into and out of the left and right temporoparietal areas (TPAs) to/from other key cortical areas affected phonological decoding in 7 dyslexic readers (DRs) and 10 typical readers (TRs) who were young adults. Granger causality was used to compute the effective connectivity of the preparatory network 500 ms prior to presentation of nonwords that required phonological decoding. Neuromagnetic activity was analyzed within the low, medium, and high beta and ga...

  8. Causality analysis in business performance measurement system using system dynamics methodology

    Science.gov (United States)

    Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

    2014-07-01

    One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

  9. mediation: R Package for Causal Mediation Analysis

    Directory of Open Access Journals (Sweden)

    Dustin Tingley

    2014-09-01

    Full Text Available In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.

  10. Reasoning with Causal Cycles

    Science.gov (United States)

    Rehder, Bob

    2017-01-01

    This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…

  11. Charged singularities: the causality violation

    Energy Technology Data Exchange (ETDEWEB)

    De Felice, F; Nobili, L [Padua Univ. (Italy). Ist. di Fisica; Calvani, M [Padua Univ. (Italy). Ist. di Astronomia

    1980-12-01

    A search is made for examples of particle trajectories which, approaching a naked singularity from infinity, make up for lost time before going back to infinity. In the Kerr-Newman metric a whole family of such trajectories is found showing that the causality violation is indeed a non-avoidable pathology.

  12. Counterfactual overdetermination vs. the causal exclusion problem.

    Science.gov (United States)

    Sparber, Georg

    2005-01-01

    This paper aims to show that a counterfactual approach to causation is not sufficient to provide a solution to the causal exclusion problem in the form of systematic overdetermination. Taking into account the truthmakers of causal counterfactuals provides a strong argument in favour of the identity of causes in situations of translevel, causation.

  13. Relações de trocas e causalidade de Granger entre preços pagos e recebidos pela agricultura brasileira, 1986/2004

    Directory of Open Access Journals (Sweden)

    Nali de Jesus de Souza

    2005-06-01

    Full Text Available O artigo examina as relações de trocas e a causalidade de Granger entre preços pagos e recebidos pela agropecuária brasileira, entre 1986 e 2004. Neste período, houve valorização das relações de trocas para o conjunto da agropecuária. Grande valorização ocorreu até 1994, com forte deterioração em 1995, mantendo-se relativamente estável até 2004. Essa valorização deveu-se ao conjunto dos produtos das lavouras, já que os produtos animais tiveram grande deterioração de suas relações de trocas. Produtos com grande deterioração foram café em coco, trigo, batata inglesa, frango e suínos. Regionalmente, as piores situações para a agropecuária, após 1994, foram as dos estados do Nordeste, exceto Maranhão e Bahia. Quanto à causalidade de Granger, os preços recebidos não causaram os preços pagos totais, nem antes nem após o Plano Real; porém, eles causaram os preços pagos pelos combustíveis, sementes e serviços antes do Plano Real e dos agrotóxicos, sementes e serviços após 1994. O efeito dos preços pagos sobre os recebidos foi maior no primeiro período do que no segundo. Com a abertura da economia, os preços agrícolas passaram a ser influenciados mais por fatores internacionais do que pelos preços internos dos insumos.This papers examines the terms of trade and Granger causality between prices received and paid by the Brazilian agricultural sector, from 1986 to 2004. During that period, the terms of trade improved for the agricultural sector. The greatest improvement occurred before 1994; in 1995 the terms of trade fell, followed by a relative stabilization until 2004. The valorization in the whole period was due to agricultural products, since animals products had a significant decline in their terms of trade. Products with the greatest losses were coffee, wheat, potatoes, chicken and hogs. Regionally, the greatest losses, after 1994, occurred in the Northeast States, with the exception of Maranhão and

  14. Confounding factors in determining causal soil moisture-precipitation feedback

    Science.gov (United States)

    Tuttle, Samuel E.; Salvucci, Guido D.

    2017-07-01

    Identification of causal links in the land-atmosphere system is important for construction and testing of land surface and general circulation models. However, the land and atmosphere are highly coupled and linked by a vast number of complex, interdependent processes. Statistical methods, such as Granger causality, can help to identify feedbacks from observational data, independent of the different parameterizations of physical processes and spatiotemporal resolution effects that influence feedbacks in models. However, statistical causal identification methods can easily be misapplied, leading to erroneous conclusions about feedback strength and sign. Here, we discuss three factors that must be accounted for in determination of causal soil moisture-precipitation feedback in observations and model output: seasonal and interannual variability, precipitation persistence, and endogeneity. The effect of neglecting these factors is demonstrated in simulated and observational data. The results show that long-timescale variability and precipitation persistence can have a substantial effect on detected soil moisture-precipitation feedback strength, while endogeneity has a smaller effect that is often masked by measurement error and thus is more likely to be an issue when analyzing model data or highly accurate observational data.

  15. Causal imprinting in causal structure learning.

    Science.gov (United States)

    Taylor, Eric G; Ahn, Woo-Kyoung

    2012-11-01

    Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. Exploring complex causal pathways between urban renewal, health and health inequality using a theory-driven realist approach.

    Science.gov (United States)

    Mehdipanah, Roshanak; Manzano, Ana; Borrell, Carme; Malmusi, Davide; Rodriguez-Sanz, Maica; Greenhalgh, Joanne; Muntaner, Carles; Pawson, Ray

    2015-01-01

    Urban populations are growing and to accommodate these numbers, cities are becoming more involved in urban renewal programs to improve the physical, social and economic conditions in different areas. This paper explores some of the complexities surrounding the link between urban renewal, health and health inequalities using a theory-driven approach. We focus on an urban renewal initiative implemented in Barcelona, the Neighbourhoods Law, targeting Barcelona's (Spain) most deprived neighbourhoods. We present evidence from two studies on the health evaluation of the Neighbourhoods Law, while drawing from recent urban renewal literature, to follow a four-step process to develop a program theory. We then use two specific urban renewal interventions, the construction of a large central plaza and the repair of streets and sidewalks, to further examine this link. In order for urban renewal programs to affect health and health inequality, neighbours must use and adapt to the changes produced by the intervention. However, there exist barriers that can result in negative outcomes including factors such as accessibility, safety and security. This paper provides a different perspective to the field that is largely dominated by traditional quantitative studies that are not always able to address the complexities such interventions provide. Furthermore, the framework and discussions serve as a guide for future research, policy development and evaluation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Developing a Causal Model of Human and Organizational Culture Factors Affecting the Knowledge Management Maturity Using Meta-Synthesis Approach

    Directory of Open Access Journals (Sweden)

    Younis Jabarzadeh

    2016-03-01

    Full Text Available Identifying influential factors which contribute to the knowledge management maturity and studying their interaction over time helps managers to understand the complex behavior of knowledge management system. It also leads them to make right decisions for utilizing these factors in promoting knowledge management and achieve strategic goals of the organization by providing a sound insight and an appropriate mechanism to reach to the optimal maturity level. In this study, all aspects and components of knowledge management with an emphasis on human factors and organizational culture, and relations between them have been identified by using a systematic literature review and meta-synthesis qualitative research approach. Then by using consultation and consensus of experts, all results verified. The results include 64 codes which are classified in 9 dimensions and two categories. Finally, due to the obtained classification and their relations, the dynamic model of knowledge management maturity is presented. The results of this study could be a suitable framework for improving mental models of knowledge management executives and experts. It makes possible Developing dynamic analysis models and appropriate policies in order to improve the knowledge management maturity in organizations.

  18. The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality

    International Nuclear Information System (INIS)

    Bekiros, Stelios D.; Diks, Cees G.H.

    2008-01-01

    The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods October 1991-October 1999 and November 1999-October 2007, with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetric GARCH effects and/or statistically significant higher order conditional moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time. (author)

  19. Is computer availability at home causally related to reading achievement in grade 4? A longitudinal difference in differences approach to IEA data from 1991 to 2006

    Directory of Open Access Journals (Sweden)

    Monica Rosén

    2016-02-01

    Full Text Available Abstract Research on effects of home computer use on children’s development of cognitive abilities and skills has yielded conflicting results, with some studies showing positive effects, others no effects, and yet others negative effects. These studies have typically used non-experimental designs and one of the main reasons for the conflicting results is that studies differ with respect to how well they control for selection bias in comparisons of children with different amounts of computer use. The current study takes advantage of data from international comparative studies of educational achievement and uses the trend design of these studies to conduct longitudinal analyses at the country level. This allows for a difference in differences approach which effectively controls for within-country selection bias, time-invariant country-level omitted variables, and random errors of measurement in the independent and dependent variables. The empirical investigations are based on data from the IEA 10-Year Trend Study and the PIRLS 2001 and 2006 studies. For these studies, information about frequency of home computer use is available in the student questionnaire. The main analytical approach employed in the paper is regression estimation based on micro-data, with fixed country effects and cluster-robust standard-errors. This approach allows estimation of main effects of home computer use and interaction effects with student characteristics (gender and socio-economic status. For both data sets negative effects of home computer use on achievement are found. Results are discussed in substantive and methodological terms, focusing particularly on possible threats to valid causal inference, such as omitted variables that are not time invariant.

  20. The dynamic relationship between structural change and CO2 emissions in Malaysia: a cointegrating approach.

    Science.gov (United States)

    Ali, Wajahat; Abdullah, Azrai; Azam, Muhammad

    2017-05-01

    The current study investigates the dynamic relationship between structural changes, real GDP per capita, energy consumption, trade openness, population density, and carbon dioxide (CO 2 ) emissions within the EKC framework over a period 1971-2013. The study used the autoregressive distributed lagged (ARDL) approach to investigate the long-run relationship between the selected variables. The study also employed the dynamic ordinary least squared (DOLS) technique to obtain the robust long-run estimates. Moreover, the causal relationship between the variables is explored using the VECM Granger causality test. Empirical results reveal a negative relationship between structural change and CO 2 emissions in the long run. The results indicate a positive relationship between energy consumption, trade openness, and CO 2 emissions. The study applied the turning point formula of Itkonen (2012) rather than the conventional formula of the turning point. The empirical estimates of the study do not support the presence of the EKC relationship between income and CO 2 emissions. The Granger causality test indicates the presence of long-run bidirectional causality between energy consumption, structural change, and CO 2 emissions in the long run. Economic growth, openness to trade, and population density unidirectionally cause CO 2 emissions. These results suggest that the government should focus more on information-based services rather than energy-intensive manufacturing activities. The feedback relationship between energy consumption and CO 2 emissions suggests that there is an ominous need to refurbish the energy-related policy reforms to ensure the installations of some energy-efficient modern technologies.

  1. Repeated causal decision making.

    Science.gov (United States)

    Hagmayer, York; Meder, Björn

    2013-01-01

    Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  2. Energy Consumption and Economic Growth in Algeria: Cointegration and Causality Analysis

    Directory of Open Access Journals (Sweden)

    Cherfi Souhila

    2012-01-01

    Full Text Available This study investigates the energy consumption-growth nexus in Algeria. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1965-2008 period is examined using the threshold cointegration and Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Algeria are non cointegrated and that there is a uni-directional causality running from LPCGDP to LPCEC, but not vice versa. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Algeria. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. The energy should be efficiently allocated into more productive sectors of the economy.

  3. Causality relation between the producer price index and the consumer price index. Ecuador Case

    Directory of Open Access Journals (Sweden)

    Víctor Quinde Rosales

    2018-01-01

    Full Text Available The present document is an investigation with a type of inductive reasoning. It evaluated the relationship of causality between the producer price index (IPP, and the consumer price index (IPC in a period from January 1998 to December 2016. The unit root test Dickey-Fuller Augmented (DFA was used under an empirical- analytic paradigm, an autoregressive vector-VAR model was generated and the Granger causality test was performed. The results show a positive trend and seasonality in the data of the variables, a VAR model of two variables was obtained with a number of optimal remnants of fourteen VAR2 (14 to which the causality test was performed, demonstrating a bi - directionality of both indices.

  4. Econometrics as evidence? Examining the 'causal' connections between financial speculation and commodities prices.

    Science.gov (United States)

    Williams, James W; Cook, Nikolai M

    2016-10-01

    One of the lasting legacies of the financial crisis of 2008, and the legislative energies that followed from it, is the growing reliance on econometrics as part of the rulemaking process. Financial regulators are increasingly expected to rationalize proposed rules using available econometric techniques, and the courts have vacated several key rules emanating from Dodd-Frank on the grounds of alleged deficiencies in this evidentiary effort. The turn toward such econometric tools is seen as a significant constraint on and challenge to regulators as they endeavor to engage with such essential policy questions as the impact of financial speculation on food security. Yet, outside of the specialized practitioner community, very little is known about these techniques. This article examines one such econometric test, Granger causality, and its role in a pivotal Dodd-Frank rulemaking. Through an examination of the test for Granger causality and its attempts to distill the causal connections between financial speculation and commodities prices, the article argues that econometrics is a blunt but useful tool, limited in its ability to provide decisive insights into commodities markets and yet yielding useful returns for those who are able to wield it.

  5. Repair of Partly Misspecified Causal Diagrams.

    Science.gov (United States)

    Oates, Chris J; Kasza, Jessica; Simpson, Julie A; Forbes, Andrew B

    2017-07-01

    Errors in causal diagrams elicited from experts can lead to the omission of important confounding variables from adjustment sets and render causal inferences invalid. In this report, a novel method is presented that repairs a misspecified causal diagram through the addition of edges. These edges are determined using a data-driven approach designed to provide improved statistical efficiency relative to de novo structure learning methods. Our main assumption is that the expert is "directionally informed," meaning that "false" edges provided by the expert would not create cycles if added to the "true" causal diagram. The overall procedure is cast as a preprocessing technique that is agnostic to subsequent causal inferences. Results based on simulated data and data derived from an observational cohort illustrate the potential for data-assisted elicitation in epidemiologic applications. See video abstract at, http://links.lww.com/EDE/B208.

  6. Repeated Causal Decision Making

    Science.gov (United States)

    Hagmayer, York; Meder, Bjorn

    2013-01-01

    Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in…

  7. Viscous causal cosmologies

    International Nuclear Information System (INIS)

    Novello, M.; Salim, J.M.; Torres, J.; Oliveira, H.P. de

    1989-01-01

    A set of spatially homogeneous and isotropic cosmological geometries generated by a class of non-perfect is investigated fluids. The irreversibility if this system is studied in the context of causal thermodynamics which provides a useful mechanism to conform to the non-violation of the causal principle. (author) [pt

  8. Causality in Classical Electrodynamics

    Science.gov (United States)

    Savage, Craig

    2012-01-01

    Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…

  9. A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China

    International Nuclear Information System (INIS)

    Chang, Ching-Chih

    2010-01-01

    This paper uses multivariate co-integration Granger causality tests to investigate the correlations between carbon dioxide emissions, energy consumption and economic growth in China. Some researchers have argued that the adoption of a reduction in carbon dioxide emissions and energy consumption as a long term policy goal will result in a closed-form relationship, to the detriment of the economy. Therefore, a perspective that can make allowances for the fact that the exclusive pursuit of economic growth will increase energy consumption and CO 2 emissions is required; to the extent that such growth will have adverse effects with regard to global climate change. (author)

  10. STRUCTURAL BREAKS, COINTEGRATION, AND CAUSALITY BY VECM ANALYSIS OF CRUDE OIL AND FOOD PRICE

    Directory of Open Access Journals (Sweden)

    Aynur Pala

    2013-01-01

    Full Text Available This papers investigated form of the linkage beetwen crude oil price index and food price index, using Johansen Cointegration test, and Granger Causality by VECM. Empirical results for monthly data from 1990:01 to 2011:08 indicated that evidence for breaks after 2008:08 and 2008:11. We find a clear long-run relationship between these series for the full and sub sample. Cointegration regression coefficient is negative at the 1990:01-2008:08 time period, but adversely positive at the 2008:11-2011:08 time period. This results represent that relation between crude oil and food price chanced.

  11. Causality in Europeanization Research

    DEFF Research Database (Denmark)

    Lynggaard, Kennet

    2012-01-01

    to develop discursive institutional analytical frameworks and something that comes close to the formulation of hypothesis on the effects of European Union (EU) policies and institutions on domestic change. Even if these efforts so far do not necessarily amount to substantive theories or claims of causality......Discourse analysis as a methodology is perhaps not readily associated with substantive causality claims. At the same time the study of discourses is very much the study of conceptions of causal relations among a set, or sets, of agents. Within Europeanization research we have seen endeavours......, it suggests that discourse analysis and the study of causality are by no means opposites. The study of Europeanization discourses may even be seen as an essential step in the move towards claims of causality in Europeanization research. This chapter deals with the question of how we may move from the study...

  12. Agency, time and causality

    Directory of Open Access Journals (Sweden)

    Thomas eWidlok

    2014-11-01

    Full Text Available Cognitive Scientists interested in causal cognition increasingly search for evidence from non-WEIRD people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition.

  13. Non-Causal Computation

    Directory of Open Access Journals (Sweden)

    Ämin Baumeler

    2017-07-01

    Full Text Available Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a novel computing paradigm beyond quantum computing, replacing this assumption by mere logical consistency: We study non-causal circuits, where a fixed time structure within a gate is locally assumed whilst the global causal structure between the gates is dropped. We present examples of logically consistent non-causal circuits outperforming all causal ones; they imply that suppressing loops entirely is more restrictive than just avoiding the contradictions they can give rise to. That fact is already known for correlations as well as for communication, and we here extend it to computation.

  14. Cointegration and Causality Test Among Export, Import, and Foreign Exchange

    Directory of Open Access Journals (Sweden)

    Haryono Subiyakto

    2016-06-01

    Full Text Available The rupiah exchange rate, import, and export are the important indicators in economy, including the Indonesia economy. The debate regarding the relationship among the exchange rate, import, and export has been persisting for several decades. Some researchers found that there is a relationship among those three and others explained that there is no correlation among them. The aim of this research is to obtain the empirical evidence of the causal relationship among the export, import, and foreign exchange rate by using the monthly data from January 2010 to April 2014. The export and import data are the export and import values in US dollar. The exchange rate data is the median exchange rates of the Indonesian Bank. The Johansen Cointegration Test and the Granger Causality Test are used to analyze the data. The research result shows that export and import have no causal relationship at five percent. Next, the foreign exchange rate influences the export and import at 10 percent level. The result indicates that the foreign exchange rate has small effects on the export and import. Based on the results, the government should control the balance of trade and should not make any policy that is based on the exchange rate values. Finally, it can be said that the exchange rate policy is not effective in increasing the exports and reducing the imports.

  15. Causality re-established.

    Science.gov (United States)

    D'Ariano, Giacomo Mauro

    2018-07-13

    Causality has never gained the status of a 'law' or 'principle' in physics. Some recent literature has even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged universality of the reversibility of the laws of physics, based either on the determinism of classical theory, or on the multiverse interpretation of quantum theory, in both cases motivated by mere interpretational requirements for realism of the theory. Here, I will show that a properly defined unambiguous notion of causality is a theorem of quantum theory, which is also a falsifiable proposition of the theory. Such a notion of causality appeared in the literature within the framework of operational probabilistic theories. It is a genuinely theoretical notion, corresponding to establishing a definite partial order among events, in the same way as we do by using the future causal cone on Minkowski space. The notion of causality is logically completely independent of the misidentified concept of 'determinism', and, being a consequence of quantum theory, is ubiquitous in physics. In addition, as classical theory can be regarded as a restriction of quantum theory, causality holds also in the classical case, although the determinism of the theory trivializes it. I then conclude by arguing that causality naturally establishes an arrow of time. This implies that the scenario of the 'block Universe' and the connected 'past hypothesis' are incompatible with causality, and thus with quantum theory: they are both doomed to remain mere interpretations and, as such, are not falsifiable, similar to the hypothesis of 'super-determinism'.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'. © 2018 The Author(s).

  16. Can chance cause cancer? A causal consideration.

    Science.gov (United States)

    Stensrud, Mats Julius; Strohmaier, Susanne; Valberg, Morten; Aalen, Odd Olai

    2017-04-01

    The role of randomness, environment and genetics in cancer development is debated. We approach the discussion by using the potential outcomes framework for causal inference. By briefly considering the underlying assumptions, we suggest that the antagonising views arise due to estimation of substantially different causal effects. These effects may be hard to interpret, and the results cannot be immediately compared. Indeed, it is not clear whether it is possible to define a causal effect of chance at all. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. The mistake of the causal relationship

    Directory of Open Access Journals (Sweden)

    О. Д. Комаров

    2015-03-01

    Full Text Available The article deals with issues of the mistake of the causal relationship. The modern criminal law science approaches to the content of the mistake of the causal relationship and its significance to the qualification of the crime are described. It is proved that in cases of dolus generalis different mental attitude of the guilty person to two separate acts of his conduct exist. Consequently, in mentioned above cases mistake of the causal relationship does not have place. The rules of qualification of the crimes commited with the mistake of causation and in cases of dolus generalis are proposed .

  18. Causality between income and emission. A country group-specific econometric analysis

    International Nuclear Information System (INIS)

    Coondoo, Dipankor; Dinda, Soumyananda

    2002-01-01

    Empirical studies of the Environmental Kuznets Curve (EKC) examine the presence or otherwise of an inverted U-shaped relationship between the level of pollution and the level of income. Customarily, in the diagram of EKC the level of income is shown on the horizontal axis and that of pollution on the vertical axis. Thus, it is presumed that the relationship between income and pollution is one of unidirectional causality with income causing environmental changes and not vice versa. The validity of this presumption is now being questioned. It is being asserted that the nature and direction of causality may vary from one country to the other. In this paper, we present the results of a study of income-CO 2 emission causality based on a Granger causality test to cross-country panel data on per capita income and the corresponding per capita CO 2 emission data. Briefly, our results indicate three different types of causality relationship holding for different country groups. For the developed country groups of North America and Western Europe (and also for Eastern Europe) the causality is found to run from emission to income. For the country groups of Central and South America, Oceania and Japan causality from income to emission is obtained. Finally, for the country groups of Asia and Africa the causality is found to be bi-directional. The regression equations estimated as part of the Granger causality test further suggest that for the country groups of North America and Western Europe the growth rate of emission has become stationary around a zero mean, and a shock in the growth rate of emission tends to generate a corresponding shock in the growth rate of income. In contrast, for the country groups of Central and South America, Oceania and Japan a shock in the income growth rate is likely to result in a corresponding shock in the growth rate of emission. Finally, causality being bi-directional for the country groups of Asia and Africa, the income and the emission growth

  19. Causality between income and emission. A country group-specific econometric analysis

    Energy Technology Data Exchange (ETDEWEB)

    Coondoo, Dipankor [Economic Research Unit, Indian Statistical Institute, 203 B.T. Road, 35 Kolkata (India); Dinda, Soumyananda [S.R. Fatepuria College, Beldanga, West Bengal, Murshidabad (India)

    2002-03-01

    Empirical studies of the Environmental Kuznets Curve (EKC) examine the presence or otherwise of an inverted U-shaped relationship between the level of pollution and the level of income. Customarily, in the diagram of EKC the level of income is shown on the horizontal axis and that of pollution on the vertical axis. Thus, it is presumed that the relationship between income and pollution is one of unidirectional causality with income causing environmental changes and not vice versa. The validity of this presumption is now being questioned. It is being asserted that the nature and direction of causality may vary from one country to the other. In this paper, we present the results of a study of income-CO{sub 2} emission causality based on a Granger causality test to cross-country panel data on per capita income and the corresponding per capita CO{sub 2} emission data. Briefly, our results indicate three different types of causality relationship holding for different country groups. For the developed country groups of North America and Western Europe (and also for Eastern Europe) the causality is found to run from emission to income. For the country groups of Central and South America, Oceania and Japan causality from income to emission is obtained. Finally, for the country groups of Asia and Africa the causality is found to be bi-directional. The regression equations estimated as part of the Granger causality test further suggest that for the country groups of North America and Western Europe the growth rate of emission has become stationary around a zero mean, and a shock in the growth rate of emission tends to generate a corresponding shock in the growth rate of income. In contrast, for the country groups of Central and South America, Oceania and Japan a shock in the income growth rate is likely to result in a corresponding shock in the growth rate of emission. Finally, causality being bi-directional for the country groups of Asia and Africa, the income and the

  20. Interactions of information transfer along separable causal paths

    Science.gov (United States)

    Jiang, Peishi; Kumar, Praveen

    2018-04-01

    Complex systems arise as a result of interdependences between multiple variables, whose causal interactions can be visualized in a time-series graph. Transfer entropy and information partitioning approaches have been used to characterize such dependences. However, these approaches capture net information transfer occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within a subgraph of interest through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [Phys. Rev. E 92, 062829 (2015), 10.1103/PhysRevE.92.062829] to develop a framework for quantifying information partitioning along separable causal paths. Momentary information transfer along causal paths captures the amount of information transfer between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique, and redundant information transfer through separable causal paths. Through a graphical model, we analyze the impact of the separable and nonseparable causal paths and the causality structure embedded in the graph as well as the noise effect on information partitioning by using synthetic data generated from two coupled logistic equation models. Our approach can provide a valuable reference for an autonomous information partitioning along separable causal paths which form a causal subgraph influencing a target.

  1. Altered causal connectivity of resting state brain networks in amnesic MCI.

    Directory of Open Access Journals (Sweden)

    Peipeng Liang

    Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

  2. The Continuum Limit of Causal Fermion Systems

    OpenAIRE

    Finster, Felix

    2016-01-01

    This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries." The dynamics is described by...

  3. Tachyons and causal paradoxes

    International Nuclear Information System (INIS)

    Maund, J.B.

    1979-01-01

    Although the existence of tachyons is not ruled out by special relativity, it appears that causal paradoxes will arise if there are tachyons. The usual solutions to these paradoxes employ some form of the reinterpretation principle. In this paper it is argued first that, the principle is incoherent, second, that even if it is not, some causal paradoxes remain, and third, the most plausible ''solution,'' which appeals to boundary conditions of the universe, will conflict with special relativity

  4. Dynamics and causality constraints

    International Nuclear Information System (INIS)

    Sousa, Manoelito M. de

    2001-04-01

    The physical meaning and the geometrical interpretation of causality implementation in classical field theories are discussed. Causality in field theory are kinematical constraints dynamically implemented via solutions of the field equation, but in a limit of zero-distance from the field sources part of these constraints carries a dynamical content that explains old problems of classical electrodynamics away with deep implications to the nature of physicals interactions. (author)

  5. Causal relationship between nuclear energy consumption and economic growth: A multi-country analysis

    International Nuclear Information System (INIS)

    Yoo, Seung-Hoon; Ku, Se-Ju

    2009-01-01

    This paper attempts to investigate the causal relationship between nuclear energy consumption and economic growth using the data from six countries among 20 countries that have used nuclear energy for more than 20 years until 2005. To this end, time-series techniques including the tests for unit roots, co-integration, and Granger-causality are employed to Argentina, France, Germany, Korea, Pakistan, and Switzerland. The main conclusion is that the causal relationship between nuclear energy consumption and economic growth is not uniform across countries. In the case of Switzerland, there exists bi-directional causality between nuclear energy consumption and economic growth. This means that an increase in nuclear energy consumption directly affects economic growth and that economic growth also stimulates further nuclear energy consumption. The uni-directional causality runs from economic growth to nuclear energy consumption without any feedback effects in France and Pakistan, and from nuclear energy to economic growth in Korea. However, any causality between nuclear energy consumption and economic growth in Argentina and Germany is not detected.

  6. Causal Links between Foreign Direct Investments and Trade: A Comparative Study of India and China

    Directory of Open Access Journals (Sweden)

    Renu SHARMA

    2013-05-01

    Full Text Available Global economic environment is changing rapidly during the last two decades. This change is reflected in widening and intensifying international linkages in trade and FDI. Various countries are now favouring economic reforms for attaining rapid and sustained growth. The scope for transnational production has expended due to reduction of the barriers to international trade and the various regional integration agreements between the different countries. This paper examines the causal relationships between FDI and trade (i.e Exports and Imports in India and China. Granger causality test has been employed to examine the causal relation between FDI and trade by using the data over the period of 1976-2011.The results for China show unidirectional causality running from FDI to imports and FDI to exports, however, there exist bidirectional causality between imports and exports. India gives the results which are not similar to China where bidirectional causality between FDI and imports; FDI and exports; and exports and imports have been found.

  7. Causality and correlations between BSE and NYSE indexes: A Janus faced relationship

    Science.gov (United States)

    Neeraj; Panigrahi, Prasanta K.

    2017-09-01

    We study the multi-scale temporal correlations and causality connections between the New York Stock Exchange (NYSE) and Bombay Stock Exchange (BSE) monthly average closing price indexes for a period of 300 months, encompassing the time period of the liberalisation of the Indian economy and its gradual global exposure. In multi-scale analysis; clearly identifiable 1, 2 and 3 year non-stationary periodic modulations in NYSE and BSE have been observed, with NYSE commensurating changes in BSE at 3 years scale. Interestingly, at one year time scale, the two exchanges are phase locked only during the turbulent times, while at the scale of three year, in-phase nature is observed for a much longer time frame. The two year time period, having characteristics of both one and three year variations, acts as the transition regime. The normalised NYSE's stock value is found to Granger cause those of BSE, with a time lag of 9 months. Surprisingly, observed Granger causality of high frequency variations reveals BSE behaviour getting reflected in the NYSE index fluctuations, after a smaller time lag. This Janus faced relationship, shows that smaller stock exchanges may provide a natural setting for simulating market fluctuations of much bigger exchanges. This possibly arises due to the fact that high frequency fluctuations form an universal part of the financial time series, and are expected to exhibit similar characteristics in open market economies.

  8. Causality discovery technology

    Science.gov (United States)

    Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

    2012-11-01

    Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

  9. Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

    Directory of Open Access Journals (Sweden)

    Zara Ghodsi

    2017-03-01

    Full Text Available In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

  10. Investigation of Causal Relationship between Stock Prices and Trading Volume using Toda and Yamamoto Procedure

    Directory of Open Access Journals (Sweden)

    Sushil BAJAJ

    2014-11-01

    Full Text Available The present study probes the relationship between the stock prices and trading volume. For achieving this purpose, daily data of adjusted closing stock prices, trading volume of 39 individual securities and S&P CNX Nifty from January 1, 1998 to May 31, 2013 have been used. In this study, instead of applying ordinary Granger causality test to investigate the relationship between stock prices and trading volume, Toda and Yamamoto (1995 procedure has been applied for analyzing the data. Lag length chosen by AIC and FPE criterion has been insured by running Lagrange Multiplier (LM test and causality determined by Toda and Yamamoto test has also been confirmed by using VAR methodology. Although, Toda and Yamamoto and VAR test produced little dissimilar results, nevertheless, the empirical analysis provides sufficient grounds to declare the presence of interaction between stock prices and trading volume.

  11. Causality Relationship Between Import, Export and Growth Rate in Developing Countries

    Directory of Open Access Journals (Sweden)

    Serhat YUKSEL

    2017-06-01

    Full Text Available In this paper, we tried to determine the relationship between imports, exports and growth rate in developing countries. Within this scope, 6 developing countries (Argentina, Brazil, China, Malaysia, Mexico and Turkey were analyzed in this study. In order to achieve this purpose, annual data for the periods between 1961 and 2014 was tested by using Engle Granger co-integration analysis, Vector Error Correction Model and Toda Yamamoto causality analysis. According to the result of the analysis, it was determined that there is not any relationship among three variables in Brazil and Mexico. On the other hand, we defined that increase in export causes higher growth rate in Argentina. Moreover, it was concluded that there is a causal relationship from import to export in China and Turkey. Furthermore, it was determined that export causes higher import in Malaysia. Therefore, it can be concluded that the relationship between import, export and growth rate is not same for all developing countries..

  12. Causal Relationship Between Islamic Bonds, Oil Price and Precious Metals: Evidence From Asia Pacific

    Directory of Open Access Journals (Sweden)

    Metadjer Widad

    2018-05-01

    Full Text Available Sukuk or Islamic bonds as new “Halal” securities had wildly expanded in Muslim and non-Muslim capital markets. So, this study aims to investigate the causal relationship between Islamic bonds (sukuk, oil and precious metals “silver and gold” prices in Asia pacific. This study used VAR model relying on daily data. The findings of Granger causality test and impulse-responses analysis results provide substantial evidence in favor of the relation between sukuk and the commodity market variables (oil, gold, and silver meanwhile and unlike many empirical studies, don’t we have found that oil doesn’t cause changes in precious metals prices. Therefore, the idea that Islamic financial markets provide diversification benefits and they are safe havens during oil crisis cannot be supported empirically.DOI: 10.15408/aiq.v10i2.7171

  13. On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth

    International Nuclear Information System (INIS)

    Apergis, Nicholas; Payne, James E.; Menyah, Kojo; Wolde-Rufael, Yemane

    2010-01-01

    This paper examines the causal relationship between CO 2 emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO 2 emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)

  14. On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth

    Energy Technology Data Exchange (ETDEWEB)

    Apergis, Nicholas [Department of Banking and Financial Management, University of Piraeus, Karaoli and Dimitriou 80, Piraeus, ATTIKI 18534 (Greece); Payne, James E. [Department of Economics, Illinois State University, Normal, IL 61790-4200 (United States); Menyah, Kojo [London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London, EC2M 6SQ (United Kingdom); Wolde-Rufael, Yemane

    2010-09-15

    This paper examines the causal relationship between CO{sub 2} emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO{sub 2} emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)

  15. A quantum causal discovery algorithm

    Science.gov (United States)

    Giarmatzi, Christina; Costa, Fabio

    2018-03-01

    Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

  16. Causal symmetric spaces

    CERN Document Server

    Olafsson, Gestur; Helgason, Sigurdur

    1996-01-01

    This book is intended to introduce researchers and graduate students to the concepts of causal symmetric spaces. To date, results of recent studies considered standard by specialists have not been widely published. This book seeks to bring this information to students and researchers in geometry and analysis on causal symmetric spaces.Includes the newest results in harmonic analysis including Spherical functions on ordered symmetric space and the holmorphic discrete series and Hardy spaces on compactly casual symmetric spacesDeals with the infinitesimal situation, coverings of symmetric spaces, classification of causal symmetric pairs and invariant cone fieldsPresents basic geometric properties of semi-simple symmetric spacesIncludes appendices on Lie algebras and Lie groups, Bounded symmetric domains (Cayley transforms), Antiholomorphic Involutions on Bounded Domains and Para-Hermitian Symmetric Spaces

  17. Causal inference in econometrics

    CERN Document Server

    Kreinovich, Vladik; Sriboonchitta, Songsak

    2016-01-01

    This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

  18. Assessment of resampling methods for causality testing: A note on the US inflation behavior

    Science.gov (United States)

    Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

    2017-01-01

    Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms. PMID:28708870

  19. Assessment of resampling methods for causality testing: A note on the US inflation behavior.

    Science.gov (United States)

    Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

    2017-01-01

    Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

  20. Productivity Analysis of Public and Private Airports: A Causal Investigation

    Science.gov (United States)

    Vasigh, Bijan; Gorjidooz, Javad

    2007-01-01

    Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is

  1. Spatial hypersurfaces in causal set cosmology

    International Nuclear Information System (INIS)

    Major, Seth A; Rideout, David; Surya, Sumati

    2006-01-01

    Within the causal set approach to quantum gravity, a discrete analogue of a spacelike region is a set of unrelated elements, or an antichain. In the continuum approximation of the theory, a moment-of-time hypersurface is well represented by an inextendible antichain. We construct a richer structure corresponding to a thickening of this antichain containing non-trivial geometric and topological information. We find that covariant observables can be associated with such thickened antichains and transitions between them, in classical sequential growth models of causal sets. This construction highlights the difference between the covariant measure on causal set cosmology and the standard sum-over-histories approach: the measure is assigned to completed histories rather than to histories on a restricted spacetime region. The resulting re-phrasing of the sum-over-histories may be fruitful in other approaches to quantum gravity

  2. Dissimilar Effects of World News Announcements on Euro/Dollar/Yen Exchange Rates: An Econophysics Approach

    Directory of Open Access Journals (Sweden)

    David Matesanz

    2015-01-01

    Full Text Available This paper revisits the issue of the influence of macro-economic announcements over the exchange rates volatility, but from a different perspective as it is the usual in the econometric literature. By quantifying the impact of world-wide macroeconomic information published in the economic calendar in several recent years we were able to construct long events’ time series with the objective to test whether they influence exchange rate volatilities in several currencies. In order to do that, Granger causality test was employed by using a computational approach. Our results show that announcements from U.S.A are, by far, the most important influence over the three spot forex quotes, Euro/Dollar, Euro/Yen and Dollar/Yen. The method proposed here opens the door to address several open questions until now.

  3. Maximally causal quantum mechanics

    International Nuclear Information System (INIS)

    Roy, S.M.

    1998-01-01

    We present a new causal quantum mechanics in one and two dimensions developed recently at TIFR by this author and V. Singh. In this theory both position and momentum for a system point have Hamiltonian evolution in such a way that the ensemble of system points leads to position and momentum probability densities agreeing exactly with ordinary quantum mechanics. (author)

  4. Causality in demand

    DEFF Research Database (Denmark)

    Nielsen, Max; Jensen, Frank; Setälä, Jari

    2011-01-01

    to fish demand. On the German market for farmed trout and substitutes, it is found that supply sources, i.e. aquaculture and fishery, are not the only determinant of causality. Storing, tightness of management and aggregation level of integrated markets might also be important. The methodological...

  5. Causality and Free Will

    Czech Academy of Sciences Publication Activity Database

    Hvorecký, Juraj

    2012-01-01

    Roč. 19, Supp.2 (2012), s. 64-69 ISSN 1335-0668 R&D Projects: GA ČR(CZ) GAP401/12/0833 Institutional support: RVO:67985955 Keywords : conciousness * free will * determinism * causality Subject RIV: AA - Philosophy ; Religion

  6. Explaining through causal mechanisms

    NARCIS (Netherlands)

    Biesbroek, Robbert; Dupuis, Johann; Wellstead, Adam

    2017-01-01

    This paper synthesizes and builds on recent critiques of the resilience literature; namely that the field has largely been unsuccessful in capturing the complexity of governance processes, in particular cause–effects relationships. We demonstrate that absence of a causal model is reflected in the

  7. A social impact assessment of the floodwater spreading project on the Gareh-Bygone plain in Iran: A causal comparative approach

    International Nuclear Information System (INIS)

    Ahmadvand, Mostafa; Karami, Ezatollah

    2009-01-01

    The purpose of this study was to explore the social impacts of the floodwater spreading project (FWSP) on the Gareh-Bygone plain, Iran. The study was in the form of a causal comparative design, and a triangulation technique was used to collect data including the use of survey data, archival data, and a participatory rural appraisal (PRA). The causal comparative method requires a comparison of villages with and without the FWSP. Therefore, a survey was conducted using stratified random sampling to select 202 households in villages with and without FWSP in the plain. Significant differences were found between the respondents in villages with and without FWSP with regard to social impact criteria. In spite of the project had negative impact on perceived wellbeing, social capital, social structure development; it had positive impact on quality of life, rural and agricultural economic conditions, and conservation of community resources. However, no significant difference was found between women and men regarding the SIA of FWSP in Gareh-Bygone plain. Analysis of the archival data and PRA techniques supported the survey results and demonstrated that the project improved environmental criteria and deteriorated social dimensions

  8. Gravity and matter in causal set theory

    International Nuclear Information System (INIS)

    Sverdlov, Roman; Bombelli, Luca

    2009-01-01

    The goal of this paper is to propose an approach to the formulation of dynamics for causal sets and coupled matter fields. We start from the continuum version of the action for a Klein-Gordon field coupled to gravity, and rewrite it first using quantities that have a direct correspondent in the case of a causal set, namely volumes, causal relations and timelike lengths, as variables to describe the geometry. In this step, the local Lagrangian density L(f;x) for a set of fields f is recast into a quasilocal expression L 0 (f;p,q) that depends on pairs of causally related points pprq and is a function of the values of f in the Alexandrov set defined by those points, and whose limit as p and q approach a common point x is L(f;x). We then describe how to discretize L 0 (f;p,q) and use it to define a causal-set-based action.

  9. Operator ordering and causality

    OpenAIRE

    Plimak, L. I.; Stenholm, S. T.

    2011-01-01

    It is shown that causality violations [M. de Haan, Physica 132A, 375, 397 (1985)], emerging when the conventional definition of the time-normal operator ordering [P.L.Kelley and W.H.Kleiner, Phys.Rev. 136, A316 (1964)] is taken outside the rotating wave approximation, disappear when the amended definition [L.P. and S.S., Annals of Physics, 323, 1989 (2008)] of this ordering is used.

  10. Space, time and causality

    International Nuclear Information System (INIS)

    Lucas, J.R.

    1984-01-01

    Originating from lectures given to first year undergraduates reading physics and philosophy or mathematics and philosophy, formal logic is applied to issues and the elucidation of problems in space, time and causality. No special knowledge of relativity theory or quantum mechanics is needed. The text is interspersed with exercises and each chapter is preceded by a suggested 'preliminary reading' and followed by 'further reading' references. (U.K.)

  11. Causality and Time in Historical Institutionalism

    DEFF Research Database (Denmark)

    Mahoney, James; Mohamedali, Khairunnisa; Nguyen, Christoph

    2016-01-01

    This chapter explores the dual concern with causality and time in historical institutionalism using a graphical approach. The analysis focuses on three concepts that are central to this field: critical junctures, gradual change, and path dependence. The analysis makes explicit and formal the logi...

  12. Causality relationship between energy demand and economic ...

    African Journals Online (AJOL)

    This paper attempts to examine the causal relationship between electricity demand and economic growth in Nigeria using data for 1970 – 2003. The study uses the Johansen cointegration VAR approach. The ADF and Phillips – Perron test statistics were used to test for stationarity of the data. It was found that the data were ...

  13. System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-06-01

    Full Text Available Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other

  14. System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism.

    Science.gov (United States)

    Wang, Lei; Sun, Xiaoliang; Weiszmann, Jakob; Weckwerth, Wolfram

    2017-01-01

    Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other cultivars revealed

  15. Optimal relaxed causal sampler using sampled-date system theory

    NARCIS (Netherlands)

    Shekhawat, Hanumant; Meinsma, Gjerrit

    This paper studies the design of an optimal relaxed causal sampler using sampled data system theory. A lifted frequency domain approach is used to obtain the existence conditions and the optimal sampler. A state space formulation of the results is also provided. The resulting optimal relaxed causal

  16. The relationship between energy consumption and economic growth in Malaysia: ARDL bound test approach

    Science.gov (United States)

    Razali, Radzuan; Khan, Habib; Shafie, Afza; Hassan, Abdul Rahman

    2016-11-01

    The objective of this paper is to examine the short-run and long-run dynamic causal relationship between energy consumption and income per capita both in bivariate and multivariate framework over the period 1971-2014 in the case of Malaysia [1]. The study applies ARDL Bound test procedure for the long run co-integration and Granger causality test for investigation of causal link between the variables. The ARDL bound test confirms the existence of long run co-integration relationship between the variables. The causality test show a feed-back hypothesis between income per capita and energy consumption over the period in the case of Malaysia.

  17. Electricity consumption and economic growth in South Africa. A trivariate causality test

    Energy Technology Data Exchange (ETDEWEB)

    Odhiambo, Nicholas M. [Economics Department, University of South Africa (UNISA), P.O. Box 392, UNISA, 0003, Pretoria (South Africa)

    2009-09-15

    In this paper we examine the causal relationship between electricity consumption and economic growth in South Africa. We incorporate the employment rate as an intermittent variable in the bivariate model between electricity consumption and economic growth - thereby creating a simple trivariate causality framework. Our empirical results show that there is a distinct bidirectional causality between electricity consumption and economic growth in South Africa. In addition, the results show that employment in South Africa Granger-causes economic growth. The results apply irrespective of whether the causality is estimated in the short-run or in the long-run formulation. The study, therefore, recommends that policies geared towards the expansion of the electricity infrastructure should be intensified in South Africa in order to cope with the increasing demand exerted by the country's strong economic growth and rapid industrialisation programme. This will certainly enable the country to avoid unprecedented power outages similar to those experienced in the country in mid-January 2008. (author)

  18. Monetary policy and the causality between inflation and money supply in Indonesia

    Directory of Open Access Journals (Sweden)

    Gatot Sasongko

    2018-05-01

    Full Text Available Conceptually and empirically, inflation volatility in Indonesia is a monetary and fiscal phenomenon. This study focuses on the macroeconomic policy and public policy especially causality between two variables namely inflation and money supply in Indonesia. This study uses Indonesian macroeconomic data of inflation and money supply from the Bank of Indonesia publication during 2007.1–2017.7. Inflation is measured by the consumer price index, reflects the annual percentage change in costs of acquiring a basket of goods and services to the average consumers that may change at specified intervals. Meanwhile, money supply is measured by the currency, demand deposits, time deposits, and saving deposits. Methodically, this study uses the Granger Causality model to determine the causality between inflation and money supply. The results show that there is a one-way causality between inflation and money supply in Indonesia. These findings imply that money supply causes inflation, but not vice versa. This condition implies that the role of Indonesian Government and Bank of Indonesia were very crucial in managing and controlling macroeconomic policy and public policy. Then, analysis of money supply and inflation also related to impacting factors such as money laundering, role of banks, taxation, tax evasion, and corruption.

  19. Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

    International Nuclear Information System (INIS)

    Xu Chuan-Ming; Yan Yan; Zhu Xiao-Wu; Li Xiao-Teng; Chen Xiao-Song

    2013-01-01

    The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007–2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance. (interdisciplinary physics and related areas of science and technology)

  20. Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

    Science.gov (United States)

    Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

    2013-11-01

    The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

  1. Quantum retrodiction and causality principle

    International Nuclear Information System (INIS)

    Shirokov, M.I.

    1994-01-01

    Quantum mechanics is factually a predictive science. But quantum retrodiction may also be needed, e.g., for the experimental verification of the validity of the Schroedinger equation for the wave function in the past if the present state is given. It is shown that in the retrodictive analog of the prediction the measurement must be replaced by another physical process called the retromeasurement. In this process, the reduction of a state vector into eigenvectors of a measured observable must proceed in the opposite direction of time as compared to the usual reduction. Examples of such processes are unknown. Moreover, they are shown to be forbidden by the causality principle stating that the later event cannot influence the earlier one. So quantum retrodiction seems to be unrealizable. It is demonstrated that the approach to the retrodiction given by S.Watanabe and F.Belinfante must be considered as an unsatisfactory ersatz of retrodicting. 20 refs., 3 figs

  2. Articulatory mediation of speech perception: a causal analysis of multi-modal imaging data.

    Science.gov (United States)

    Gow, David W; Segawa, Jennifer A

    2009-02-01

    The inherent confound between the organization of articulation and the acoustic-phonetic structure of the speech signal makes it exceptionally difficult to evaluate the competing claims of motor and acoustic-phonetic accounts of how listeners recognize coarticulated speech. Here we use Granger causation analyzes of high spatiotemporal resolution neural activation data derived from the integration of magnetic resonance imaging, magnetoencephalography and electroencephalography, to examine the role of lexical and articulatory mediation in listeners' ability to use phonetic context to compensate for place assimilation. Listeners heard two-word phrases such as pen pad and then saw two pictures, from which they had to select the one that depicted the phrase. Assimilation, lexical competitor environment and the phonological validity of assimilation context were all manipulated. Behavioral data showed an effect of context on the interpretation of assimilated segments. Analysis of 40 Hz gamma phase locking patterns identified a large distributed neural network including 16 distinct regions of interest (ROIs) spanning portions of both hemispheres in the first 200 ms of post-assimilation context. Granger analyzes of individual conditions showed differing patterns of causal interaction between ROIs during this interval, with hypothesized lexical and articulatory structures and pathways driving phonetic activation in the posterior superior temporal gyrus in assimilation conditions, but not in phonetically unambiguous conditions. These results lend strong support for the motor theory of speech perception, and clarify the role of lexical mediation in the phonetic processing of assimilated speech.

  3. Causal events enter awareness faster than non-causal events

    Directory of Open Access Journals (Sweden)

    Pieter Moors

    2017-01-01

    Full Text Available Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967. Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946. Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world.

  4. Analogy in causal inference: rethinking Austin Bradford Hill's neglected consideration.

    Science.gov (United States)

    Weed, Douglas L

    2018-05-01

    The purpose of this article was to rethink and resurrect Austin Bradford Hill's "criterion" of analogy as an important consideration in causal inference. In epidemiology today, analogy is either completely ignored (e.g., in many textbooks), or equated with biologic plausibility or coherence, or aligned with the scientist's imagination. None of these examples, however, captures Hill's description of analogy. His words suggest that there may be something gained by contrasting two bodies of evidence, one from an established causal relationship, the other not. Coupled with developments in the methods of systematic assessments of evidence-including but not limited to meta-analysis-analogy can be restructured as a key component in causal inference. This new approach will require that a collection-a library-of known cases of causal inference (i.e., bodies of evidence involving established causal relationships) be developed. This library would likely include causal assessments by organizations such as the International Agency for Research on Cancer, the National Toxicology Program, and the United States Environmental Protection Agency. In addition, a process for describing key features of a causal relationship would need to be developed along with what will be considered paradigm cases of causation. Finally, it will be important to develop ways to objectively compare a "new" body of evidence with the relevant paradigm case of causation. Analogy, along with all other existing methods and causal considerations, may improve our ability to identify causal relationships. Copyright © 2018 Elsevier Inc. All rights reserved.

  5. Determinant and impacts of dynamic inflation in Ethiopia

    OpenAIRE

    Biresaw, Temesgen Tezera

    2014-01-01

    This thesis uses quarterly data for the period 1998-2010 to investigate the determinant and impacts of dynamic inflation in Ethiopia. By using Granger causality model approach four testable hypotheses are investigated: (1) does the money supply growth Granger-cause inflation? (2) Does currency devaluation Granger cause inflation? (3) Does inflation affect economic growth? And (4) Does oil price Granger cause of inflation? The empirical results suggest that there existed a bi-directional ...

  6. A causality between fund performance and stock market

    Science.gov (United States)

    Kim, Ho-Yong; Kwon, Okyu; Oh, Gabjin

    2016-02-01

    We investigate whether the characteristic fund performance indicators (FPI), such as the fund return, the Net asset value (NAV) and the cash flow, are correlated with the asset price movement using information flows estimated by the Granger causality test. First, we find that the information flow of FPI is most sensitive to extreme events of the Korean stock market, which include negative events such as the sub-prime crisis and the impact of QE (quantitative easing) by the US subprime and Europe financial crisis as well as the positive events of the golden period of Korean Composite Stock Price Index (KOSPI), except for the fund cash flow. Second, both the fund return and the NAV exhibit significant correlations with the KOSPI, whereas the cash flow is not correlated with the stock market. This result suggests that the information resulting from the ability of the fund manager should influence stock market. Finally, during market crisis period, information flows between FPI and the Korean stock market are significantly positively correlated with the market volatility.

  7. Causal Diagrams for Empirical Research

    OpenAIRE

    Pearl, Judea

    1994-01-01

    The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifiying causal effects from non-experimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in ter...

  8. Causality Statistical Perspectives and Applications

    CERN Document Server

    Berzuini, Carlo; Bernardinell, Luisa

    2012-01-01

    A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book:Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addr

  9. Causal electromagnetic interaction equations

    International Nuclear Information System (INIS)

    Zinoviev, Yury M.

    2011-01-01

    For the electromagnetic interaction of two particles the relativistic causal quantum mechanics equations are proposed. These equations are solved for the case when the second particle moves freely. The initial wave functions are supposed to be smooth and rapidly decreasing at the infinity. This condition is important for the convergence of the integrals similar to the integrals of quantum electrodynamics. We also consider the singular initial wave functions in the particular case when the second particle mass is equal to zero. The discrete energy spectrum of the first particle wave function is defined by the initial wave function of the free-moving second particle. Choosing the initial wave functions of the free-moving second particle it is possible to obtain a practically arbitrary discrete energy spectrum.

  10. Causal learning and inference as a rational process: the new synthesis.

    Science.gov (United States)

    Holyoak, Keith J; Cheng, Patricia W

    2011-01-01

    Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

  11. Structural Equations and Causal Explanations: Some Challenges for Causal SEM

    Science.gov (United States)

    Markus, Keith A.

    2010-01-01

    One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

  12. Cointegration and causality analysis of dynamic linkage between stock market and equity mutual funds in Australia

    Directory of Open Access Journals (Sweden)

    Sasipa Pojanavatee

    2014-12-01

    Full Text Available The existing literature finds conflicting results on the magnitude of price linkages between equity mutual funds and the stock market. The study contends that in an optimal lagged model, the expectations of future prices using knowledge of past price behaviour in a particular equity mutual fund category will improve forecasts of prices of other equity mutual fund categories and the stock market index. The evidence shows that the long-run pricing of equity mutual funds is cointegrated with the stock market index. In the short run, the results indicate that some equity mutual fund categories possess both long-run and short-run exogeneity with the stock market. Therefore, the short-run dynamic indicates short-run Granger causal links running between different equity mutual fund categories.

  13. Differences in hemispherical thalamo-cortical causality analysis during resting-state fMRI.

    Science.gov (United States)

    Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Wolff, Stephan; Deuschl, Guunther; Heute, Ulrich; Muthuraman, Muthuraman

    2014-01-01

    Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC) to investigate the functional connectivity. Results of CGC analysis revealed the asymmetry between connection strengths of the bilateral thalamus. Upon testing the functional connectivity of the default-mode network (DMN) at low-frequency fluctuations (LFF) and comparing coherence vectors using Spearman's rank correlation, we found that thalamus is a better source for the signals directed towards the contralateral regions of the brain, however, when thalamus acts as sink, it is a better sink for signals generated from ipsilateral regions of the brain.

  14. The causal structure of utility conditionals.

    Science.gov (United States)

    Bonnefon, Jean-François; Sloman, Steven A

    2013-01-01

    The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ''if p then q'' statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. Copyright © 2012 Cognitive Science Society, Inc.

  15. On minimizers of causal variational principles

    International Nuclear Information System (INIS)

    Schiefeneder, Daniela

    2011-01-01

    Causal variational principles are a class of nonlinear minimization problems which arise in a formulation of relativistic quantum theory referred to as the fermionic projector approach. This thesis is devoted to a numerical and analytic study of the minimizers of a general class of causal variational principles. We begin with a numerical investigation of variational principles for the fermionic projector in discrete space-time. It is shown that for sufficiently many space-time points, the minimizing fermionic projector induces non-trivial causal relations on the space-time points. We then generalize the setting by introducing a class of causal variational principles for measures on a compact manifold. In our main result we prove under general assumptions that the support of a minimizing measure is either completely timelike, or it is singular in the sense that its interior is empty. In the examples of the circle, the sphere and certain flag manifolds, the general results are supplemented by a more detailed analysis of the minimizers. (orig.)

  16. Expert Causal Reasoning and Explanation.

    Science.gov (United States)

    Kuipers, Benjamin

    The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

  17. Re-thinking local causality

    NARCIS (Netherlands)

    Friederich, Simon

    There is widespread belief in a tension between quantum theory and special relativity, motivated by the idea that quantum theory violates J. S. Bell's criterion of local causality, which is meant to implement the causal structure of relativistic space-time. This paper argues that if one takes the

  18. Covariation in Natural Causal Induction.

    Science.gov (United States)

    Cheng, Patricia W.; Novick, Laura R.

    1991-01-01

    Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)

  19. World oil and agricultural commodity prices: Evidence from nonlinear causality

    International Nuclear Information System (INIS)

    Nazlioglu, Saban

    2011-01-01

    The increasing co-movements between the world oil and agricultural commodity prices have renewed interest in determining price transmission from oil prices to those of agricultural commodities. This study extends the literature on the oil-agricultural commodity prices nexus, which particularly concentrates on nonlinear causal relationships between the world oil and three key agricultural commodity prices (corn, soybeans, and wheat). To this end, the linear causality approach of Toda-Yamamoto and the nonparametric causality method of Diks-Panchenko are applied to the weekly data spanning from 1994 to 2010. The linear causality analysis indicates that the oil prices and the agricultural commodity prices do not influence each other, which supports evidence on the neutrality hypothesis. In contrast, the nonlinear causality analysis shows that: (i) there are nonlinear feedbacks between the oil and the agricultural prices, and (ii) there is a persistent unidirectional nonlinear causality running from the oil prices to the corn and to the soybeans prices. The findings from the nonlinear causality analysis therefore provide clues for better understanding the recent dynamics of the agricultural commodity prices and some policy implications for policy makers, farmers, and global investors. This study also suggests the directions for future studies. - Research highlights: → This study determines the price transmission mechanisms between the world oil and three key agricultural commodity prices (corn, soybeans, and wheat). → The linear and nonlinear cointegration and causality methods are carried out. → The linear causality analysis supports evidence on the neutrality hypothesis. → The nonlinear causality analysis shows that there is a persistent unidirectional causality from the oil prices to the corn and to the soybeans prices.

  20. On causality of extreme events

    Directory of Open Access Journals (Sweden)

    Massimiliano Zanin

    2016-06-01

    Full Text Available Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

  1. Energy Consumption and Economic Growth in Vietnam: Threshold Cointegration and Causality Analysis

    Directory of Open Access Journals (Sweden)

    BINH Thanh PHUNG

    2011-01-01

    Full Text Available This study investigates the energy consumption-growth nexus in Vietnam. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1976-2010 period is examined using the threshold cointegration and vector error correction models for Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Vietnam are cointegrated and that there is a strong uni-directional causality running from LPCGDP to LPCEC, but not vice versa. It is also found that the effect of LPCGDP on LPCEC in Vietnam is time-varying (i.e. significantly different between before and after the structural breakpoint, 1992. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Vietnam. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. In future, the energy should be efficiently allocated into more productive sectors of the economy.

  2. The causality relationship between energy consumption and GDP in G-11 countries revisited

    International Nuclear Information System (INIS)

    Lee, C.-C.

    2006-01-01

    This paper explores whether energy conservation policies can be implemented in countries with the same level of development. That is, is restraining energy consumption without compromising economic growth feasible in all industrialized countries? A new Granger non-causality testing procedure developed by Toda and Yamamoto [1995, Journal of Econometrics 66, 225-250] is applied to re-investigate the relationship, if any, between energy consumption and income in 11 major industrialized countries. The results clearly do not support the view that energy consumption and income are neutral with respect to each other, except in the case of the United Kingdom, Germany and Sweden where a neutral relationship is found. Bi-directional causality in the United States and uni-directional running from energy consumption to GDP in Canada, Belgium, the Netherlands and Switzerland are found. This indicates that energy conservation may hinder economic growth in the latter five countries. Further, the causality relationship appears to be uni-directional but reversed for France, Italy and Japan which implies that, in these three countries, energy conservation may be viable without being detrimental to economic growth

  3. Neural Correlates of Causal Power Judgments

    Directory of Open Access Journals (Sweden)

    Denise Dellarosa Cummins

    2014-12-01

    Full Text Available Causal inference is a fundamental component of cognition and perception. Probabilistic theories of causal judgment (most notably causal Bayes networks derive causal judgments using metrics that integrate contingency information. But human estimates typically diverge from these normative predictions. This is because human causal power judgments are typically strongly influenced by beliefs concerning underlying causal mechanisms, and because of the way knowledge is retrieved from human memory during the judgment process. Neuroimaging studies indicate that the brain distinguishes causal events from mere covariation, and between perceived and inferred causality. Areas involved in error prediction are also activated, implying automatic activation of possible exception cases during causal decision-making.

  4. Clear message for causality

    Energy Technology Data Exchange (ETDEWEB)

    Steinberg, Aephraim M. [Institute for Experimental Physics, University of Vienna, Vienna (Austria)

    2003-12-01

    Experiment confirms that information cannot be transmitted faster than the speed of light. Ever since Einstein stated that nothing can travel faster than light, physicists have delighted in finding exceptions. One after another, observations of such 'superluminal' propagation have been made. However, while some image or pattern- such as the motion of a spotlight projected on a distant wall - might have appeared to travel faster than light, it seemed that there was no way to use the superluminal effect to transmit energy or information. In recent years, the superluminal propagation of light pulses through certain media has led to renewed controversy. In 1995, for example, Guenther Nimtz of the University of Cologne encoded Mozart's 40th Symphony on a microwave beam, which he claimed to have transmitted at a speed faster than light. Others maintain that such a violation of Einstein's speed limit would wreak havoc on our most fundamental ideas about causality, allowing an effect to precede its cause. Relativity teaches us that sending a signal faster than light would be equivalent to sending it backwards in time. (U.K.)

  5. Causal Rasch models

    Directory of Open Access Journals (Sweden)

    A. Jackson Stenner

    2013-08-01

    Full Text Available Rasch’s unidimensional models for measurement show how to connect object measures (e.g., reader abilities, measurement mechanisms (e.g., machine-generated cloze reading items, and observational outcomes (e.g., counts correct on reading instruments. Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct. We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

  6. Causal Rasch models.

    Science.gov (United States)

    Stenner, A Jackson; Fisher, William P; Stone, Mark H; Burdick, Donald S

    2013-01-01

    Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

  7. Causal Rasch models

    Science.gov (United States)

    Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

    2013-01-01

    Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

  8. Causal aspects of diffraction

    International Nuclear Information System (INIS)

    Crawford, G.N.

    1981-01-01

    The analysis is directed at a causal description of photon diffraction, which is explained in terms of a wave exerting real forces and providing actual guidance to each quantum of energy. An undulatory PSI wave is associated with each photon, and this wave is assumed to imply more than an informative probability function, so that it actually carries real energy, in much the same way as does an electro-magnetic wave. Whether or not it may be in some way related to the electromagnetic wave is left as a matter of on-going concern. A novel application of the concept of a minimum energy configuration is utilized; that is, a system of energy quanta seeks out relative positions and orientations of least mutual energy, much as an electron seeks its Bohr radius as a position of least mutual energy. Thus the concept implies more a guiding interaction of the PSI waves than an interfering cancellation of these waves. Similar concepts have been suggested by L. de Broglie and D. Bohm

  9. ["Karoshi" and causal relationships].

    Science.gov (United States)

    Hamajima, N

    1992-08-01

    This paper aims to introduce a measure for use by physicians for stating the degree of probable causal relationship for "Karoshi", ie, a sudden death from cerebrovascular diseases or ischemic heart diseases under occupational stresses, as well as to give a brief description for legal procedures associated with worker's compensation and civil trial in Japan. It is a well-used measure in epidemiology, "attributable risk percent (AR%)", which can be applied to describe the extent of contribution to "Karoshi" of the excess occupational burdens the deceased worker was forced to bear. Although several standards such as average occupational burdens for the worker, average occupational burdens for an ordinary worker, burdens in a nonoccupational life, and a complete rest, might be considered for the AR% estimation, the average occupational burdens for an ordinary worker should normally be utilized as a standard for worker's compensation. The adoption of AR% could be helpful for courts to make a consistent judgement whether "Karoshi" cases are compensatable or not.

  10. Structure and Strength in Causal Induction

    Science.gov (United States)

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2005-01-01

    We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…

  11. Dynamics of Quantum Causal Structures

    Science.gov (United States)

    Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav

    2018-01-01

    It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

  12. Dynamics of Quantum Causal Structures

    Directory of Open Access Journals (Sweden)

    Esteban Castro-Ruiz

    2018-03-01

    Full Text Available It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B. Here, we develop a framework for “dynamics of causal structures,” i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B, via superposition of causal orders, to a channel from B to A. We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

  13. Principal stratification in causal inference.

    Science.gov (United States)

    Frangakis, Constantine E; Rubin, Donald B

    2002-03-01

    Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

  14. Causal boundary for stably causal space-times

    International Nuclear Information System (INIS)

    Racz, I.

    1987-12-01

    The usual boundary constructions for space-times often yield an unsatisfactory boundary set. This problem is reviewed and a new solution is proposed. An explicit identification rule is given on the set of the ideal points of the space-time. This construction leads to a satisfactory boundary point set structure for stably causal space-times. The topological properties of the resulting causal boundary construction are examined. For the stably causal space-times each causal curve has a unique endpoint on the boundary set according to the extended Alexandrov topology. The extension of the space-time through the boundary is discussed. To describe the singularities the defined boundary sets have to be separated into two disjoint sets. (D.Gy.) 8 refs

  15. Causal boundary for strongly causal spacetimes: Pt. 1

    International Nuclear Information System (INIS)

    Szabados, L.B.

    1989-01-01

    In a previous paper an analysis of the general structure of the causal boundary constructions and a new explicit identification rule, built up from elementary TIP-TIF gluings, were presented. In the present paper we complete our identification by incorporating TIP-TIP and TIF-TIF gluings as well. An asymptotic causality condition is found which, for physically important cases, ensures the uniqueness of the endpoints of the non-spacelike curves in the completed spacetime. (author)

  16. Classical planning and causal implicatures

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Benotti, Luciana

    In this paper we motivate and describe a dialogue manager (called Frolog) which uses classical planning to infer causal implicatures. A causal implicature is a type of Gricean relation implicature, a highly context dependent form of inference. As we shall see, causal implicatures are important...... to generate clarification requests"; as a result we can model task-oriented dialogue as an interactive process locally structured by negotiation of the underlying task. We give several examples of Frolog-human dialog, discuss the limitations imposed by the classical planning paradigm, and indicate...

  17. Functional equations with causal operators

    CERN Document Server

    Corduneanu, C

    2003-01-01

    Functional equations encompass most of the equations used in applied science and engineering: ordinary differential equations, integral equations of the Volterra type, equations with delayed argument, and integro-differential equations of the Volterra type. The basic theory of functional equations includes functional differential equations with causal operators. Functional Equations with Causal Operators explains the connection between equations with causal operators and the classical types of functional equations encountered by mathematicians and engineers. It details the fundamentals of linear equations and stability theory and provides several applications and examples.

  18. A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test

    Science.gov (United States)

    Polanco-Martínez, J. M.; Fernández-Macho, J.; Neumann, M. B.; Faria, S. H.

    2018-01-01

    This paper presents an analysis of EU peripheral (so-called PIIGS) stock market indices and the S&P Europe 350 index (SPEURO), as a European benchmark market, over the pre-crisis (2004-2007) and crisis (2008-2011) periods. We computed a rolling-window wavelet correlation for the market returns and applied a non-linear Granger causality test to the wavelet decomposition coefficients of these stock market returns. Our results show that the correlation is stronger for the crisis than for the pre-crisis period. The stock market indices from Portugal, Italy and Spain were more interconnected among themselves during the crisis than with the SPEURO. The stock market from Portugal is the most sensitive and vulnerable PIIGS member, whereas the stock market from Greece tends to move away from the European benchmark market since the 2008 financial crisis till 2011. The non-linear causality test indicates that in the first three wavelet scales (intraweek, weekly and fortnightly) the number of uni-directional and bi-directional causalities is greater during the crisis than in the pre-crisis period, because of financial contagion. Furthermore, the causality analysis shows that the direction of the Granger cause-effect for the pre-crisis and crisis periods is not invariant in the considered time-scales, and that the causality directions among the studied stock markets do not seem to have a preferential direction. These results are relevant to better understand the behaviour of vulnerable stock markets, especially for investors and policymakers.

  19. Causal Modelling in Evaluation Research.

    Science.gov (United States)

    Winteler, Adolf

    1983-01-01

    A study applied path analysis methods, using new techniques of causal analysis, to the problem of predicting the achievement, dropout rate, and satisfaction of university students. Besides providing explanations, the technique indicates possible remedial measures. (MSE)

  20. Consciousness and the "Causal Paradox"

    OpenAIRE

    Velmans, Max

    1996-01-01

    Viewed from a first-person perspective consciousness appears to be necessary for complex, novel human activity - but viewed from a third-person perspective consciousness appears to play no role in the activity of brains, producing a "causal paradox". To resolve this paradox one needs to distinguish consciousness of processing from consciousness accompanying processing or causing processing. Accounts of consciousness/brain causal interactions switch between first- and third-person perspectives...

  1. Birth weight differences between those offered financial voucher incentives for verified smoking cessation and control participants enrolled in the Cessation in Pregnancy Incentives Trial (CPIT), employing an intuitive approach and a Complier Average Causal Effects (CACE) analysis.

    Science.gov (United States)

    McConnachie, Alex; Haig, Caroline; Sinclair, Lesley; Bauld, Linda; Tappin, David M

    2017-07-20

    The Cessation in Pregnancy Incentives Trial (CPIT), which offered financial incentives for smoking cessation during pregnancy showed a clinically and statistically significant improvement in cessation. However, infant birth weight was not seen to be affected. This study re-examines birth weight using an intuitive and a complier average causal effects (CACE) method to uncover important information missed by intention-to-treat analysis. CPIT offered financial incentives up to £400 to pregnant smokers to quit. With incentives, 68 women (23.1%) were confirmed non-smokers at primary outcome, compared to 25 (8.7%) without incentives, a difference of 14.3% (Fisher test, p financial incentives to quit. Viewed in this way, the overall birth weight gain with incentives is attributable only to potential quitters. We compared an intuitive approach to a CACE analysis. Mean birth weight of potential quitters in the incentives intervention group (who therefore quit) was 3338 g compared with potential quitters in the control group (who did not quit) 3193 g. The difference attributable to incentives, was 3338 - 3193 = 145 g (95% CI -617, +803). The mean difference in birth weight between the intervention and control groups was 21 g, and the difference in the proportion who managed to quit was 14.3%. Since the intervention consisted of the offer of incentives to quit smoking, the intervention was received by all women in the intervention group. However, "compliance" was successfully quitting with incentives, and the CACE analysis yielded an identical result, causal birth weight increase 21 g ÷ 0.143 = 145 g. Policy makers have great difficulty giving pregnant women money to stop smoking. This study indicates that a small clinically insignificant improvement in average birth weight is likely to hide an important clinically significant increase in infants born to pregnant smokers who want to stop but cannot achieve smoking cessation without the addition of financial

  2. THE METHODOLOGY OF TESTING THE CAUSALITY BETWEEN THE ROMANIAN MUTUAL FUNDS MARKET AND THE ECONOMY’S DYNAMICS

    Directory of Open Access Journals (Sweden)

    Ioana RADU

    2013-06-01

    Full Text Available The paper tests and evaluates the causality between the dynamics of the Romanian mutual fund market and the economy. Using the Granger causality test, a regression analysis has been developed on quarterly data during 2004Q3 – 2012Q2 for the Romanian economy. Based on this relationship, we can emphasize that the controversial debate upon the economic growth and the mutual fund market has became a complex research subject. Therefore, due to its complexity, the timeliness and the continuous growth of the investment funds area, this paper complements the existing literature by identifying the causal linkage between the mutual fund market and the economy. The paper is organized as it follows. First part presents the main premises that have emphasized our research. Second part presents a brief literature review and extracts the studies that appreciate best the relationship between the analyzed variables. Next section is set on defining the potential correlation between the analyzed variables. Then, section 4 tests the causality by using the R facility. The last part concludes.

  3. CO2 emissions and financial development in an emerging economy: An augmented VAR approach

    International Nuclear Information System (INIS)

    Abbasi, Faiza; Riaz, Khalid

    2016-01-01

    This paper explores the influence of economic and financial development on carbon emissions in a small emerging economy. The study employs ARDL approach to investigate the long run relationship between carbon emissions and a set of economic and financial variables, an Error Correction Model (ECM) to capture the short run dynamics, Granger causality in an augmented VAR framework to check the causality direction, and variance decomposition based on an estimated Vector Error Correction Model (VECM) to determine the relative contributions of economic and financial variables to the evolution of per capita carbon emissions. The periods considered were the full sample (1971–2011), and a reduced sample sub-period (1988–2011) that corresponded to greater liberalization and financial sector development. The financial variables played a role in emission mitigation only in the latter period where greater degree of liberalization and financial sector development occurred. Even then the relative magnitude of emissions mitigation attributable to financial variables was much smaller compared to the emissions raising impact of rising per capita incomes. This underscores the need for adopting other mitigation policies for reducing carbon footprints in those emerging economies where a sufficient degree of financial deepening and financial sector development has not yet taken place. - Highlights: • In economies where structural transformation of financial sector is in early stages. • Financial development does not aid in mitigating CO 2 emissions rather it increases it. • CO 2 emissions rise as per capita income rises. • Government should devise comprehensive and realistic mitigation strategies.

  4. Concepts in causality: chemically induced human urinary bladder cancer

    International Nuclear Information System (INIS)

    Lower, G.M. Jr.

    1982-01-01

    A significant portion of the incidence of human urinary bladder cancer can be attributed to occupational and cultural (tobacco smoking) situations associated with exposures to various arylamines, many of which represent established human carcinogens. A brief historical overview of research in bladder cancer causality indicates that the identification of causal agents and causal mechanism has been approached and rests upon information gathered at the organismal (geographical/historical), cellular, and molecular levels of biologic organization. This viewpoint speaks of a natural evolution within the biomedical sciences; a natural evolution from descriptive approaches to mechanistic approaches; and a natural evolution from more or less independent discipline-oriented approaches to hierarchically organized multidisciplinary approaches. Available information relevant to bladder cancer causality can be readily integrated into general conceptual frameworks to yield a hierarchial view of the natural history of urinary bladder cancer, a view consistent with contemporary natural systems and information theory and perhaps relevant also to other chemically induced epithelial cancers. Such frameworks are useful in appreciating the spatial and temporal boundaries and interrelationships in causality and the conceptual interrelationships within the biomedical sciences. Recent approaches in molecular epidemiology and the assessment of relative individual susceptibility to bladder cancer indicate that such frameworks are useful in forming hypotheses

  5. Non-Gaussian Methods for Causal Structure Learning.

    Science.gov (United States)

    Shimizu, Shohei

    2018-05-22

    Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

  6. Curvature constraints from the causal entropic principle

    International Nuclear Information System (INIS)

    Bozek, Brandon; Albrecht, Andreas; Phillips, Daniel

    2009-01-01

    Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The causal entropic principle (Bousso et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the causal entropic principle to predict the preferred curvature within the 'multiverse'. We have found that values larger than ρ k =40ρ m are disfavored by more than 99.99% peak value at ρ Λ =7.9x10 -123 and ρ k =4.3ρ m for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.

  7. Causal inference of asynchronous audiovisual speech

    Directory of Open Access Journals (Sweden)

    John F Magnotti

    2013-11-01

    Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.

  8. Regression to Causality : Regression-style presentation influences causal attribution

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  9. Putting a cap on causality violations in causal dynamical triangulations

    International Nuclear Information System (INIS)

    Ambjoern, Jan; Loll, Renate; Westra, Willem; Zohren, Stefan

    2007-01-01

    The formalism of causal dynamical triangulations (CDT) provides us with a non-perturbatively defined model of quantum gravity, where the sum over histories includes only causal space-time histories. Path integrals of CDT and their continuum limits have been studied in two, three and four dimensions. Here we investigate a generalization of the two-dimensional CDT model, where the causality constraint is partially lifted by introducing branching points with a weight g s , and demonstrate that the system can be solved analytically in the genus-zero sector. The solution is analytic in a neighborhood around weight g s = 0 and cannot be analytically continued to g s = ∞, where the branching is entirely geometric and where one would formally recover standard Euclidean two-dimensional quantum gravity defined via dynamical triangulations or Liouville theory

  10. Causality and analyticity in optics

    International Nuclear Information System (INIS)

    Nussenzveig, H.M.

    In order to provide an overall picture of the broad range of optical phenomena that are directly linked with the concepts of causality and analyticity, the following topics are briefly reviewed, emphasizing recent developments: 1) Derivation of dispersion relations for the optical constants of general linear media from causality. Application to the theory of natural optical activity. 2) Derivation of sum rules for the optical constants from causality and from the short-time response function (asymptotic high-frequency behavior). Average spectral behavior of optical media. Applications. 3) Role of spectral conditions. Analytic properties of coherence functions in quantum optics. Reconstruction theorem.4) Phase retrieval problems. 5) Inverse scattering problems. 6) Solution of nonlinear evolution equations in optics by inverse scattering methods. Application to self-induced transparency. Causality in nonlinear wave propagation. 7) Analytic continuation in frequency and angular momentum. Complex singularities. Resonances and natural-mode expansions. Regge poles. 8) Wigner's causal inequality. Time delay. Spatial displacements in total reflection. 9) Analyticity in diffraction theory. Complex angular momentum theory of Mie scattering. Diffraction as a barrier tunnelling effect. Complex trajectories in optics. (Author) [pt

  11. Hierarchical organisation of causal graphs

    International Nuclear Information System (INIS)

    Dziopa, P.

    1993-01-01

    This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs

  12. Assessment of 48 Stock markets using adaptive multifractal approach

    Science.gov (United States)

    Ferreira, Paulo; Dionísio, Andreia; Movahed, S. M. S.

    2017-11-01

    In this paper, Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Since underlying data sets are affected by non-stationarities and trends, we also apply Adaptive Multifractal Detrended Fluctuation Analysis (AMF-DFA) and Adaptive Multifractal Detrended Cross-Correlation Analysis (AMF-DXA). We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, h(q = 2) > 1, we find that all underlying data sets belong to non-stationary process. According to Efficient Market Hypothesis (EMH), only 8 markets are classified in uncorrelated processes at 2 σ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with H = 0 . 457 ± 0 . 004 and Jordan with H = 0 . 602 ± 0 . 006 are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, Hxy ≤ [Hxx +Hyy ] / 2, is confirmed. Mentioned relation for q > 0 is also satisfied while for q behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local (internal) and global (external) conditions. Width of singularity spectrum for auto-correlation and cross-correlation are Δαxx ∈ [ 0 . 304 , 0 . 905 ] and Δαxy ∈ [ 0 . 246 , 1 . 178 ] , respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of σDCCA indicates that all pairs of stock market studied in this time interval

  13. Entropy for theories with indefinite causal structure

    International Nuclear Information System (INIS)

    Markes, Sonia; Hardy, Lucien

    2011-01-01

    Any theory with definite causal structure has a defined past and future, be it defined by light cones or an absolute time scale. Entropy is a concept that has traditionally been reliant on a definite notion of causality. However, without a definite notion of causality, the concept of entropy is not all lost. Indefinite causal structure results from combining probabilistic predictions and dynamical space-time. The causaloid framework lays the mathematical groundwork to be able to treat indefinite causal structure. In this paper, we build on the causaloid mathematics and define a causally-unbiased entropy for an indefinite causal structure. In defining a causally-unbiased entropy, there comes about an emergent idea of causality in the form of a measure of causal connectedness, termed the Q factor.

  14. mediation: R package for causal mediation analysis

    OpenAIRE

    Tingley, Dustin; Yamamoto, Teppei; Hirose, Kentaro; Keele, Luke; Imai, Kosuke

    2012-01-01

    In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting su...

  15. A Causal Theory of Modality

    Directory of Open Access Journals (Sweden)

    José Tomás Alvarado

    2009-08-01

    Full Text Available This work presents a causal conception of metaphysical modality in which a state of affairs is metaphysically possible if and only if it can be caused (in the past, the present or the future by current entities. The conception is contrasted with what is called the “combinatorial” conception of modality, in which everything can co-exist with anything else. This work explains how the notion of ‘causality’ should be construed in the causal theory, what difference exists between modalities thus defined from nomological modality, how accessibility relations between possible worlds should be interpreted, and what is the relation between the causal conception and the necessity of origin.

  16. Introductive remarks on causal inference

    Directory of Open Access Journals (Sweden)

    Silvana A. Romio

    2013-05-01

    Full Text Available One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.

  17. Causal reasoning with mental models

    Science.gov (United States)

    Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

    2014-01-01

    This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

  18. Causal reasoning with mental models.

    Science.gov (United States)

    Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

    2014-01-01

    This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

  19. Causal reasoning with mental models

    Directory of Open Access Journals (Sweden)

    Sangeet eKhemlani

    2014-10-01

    Full Text Available This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

  20. The problem of causality in corporate governance research: The case of governance indexes and firm valuation

    Directory of Open Access Journals (Sweden)

    Jimmy A. Saravia

    2017-09-01

    Full Text Available In recent years the problem of the determination of causality has become an increasingly important question in the field of corporate governance. This paper reviews contemporary literature on the topic of causality, specifically it examines the literature that investigates the causal relationship between corporate governance indexes and firm valuation and finds that the current approach is to attempt to determine causality empirically and that the problem remains unresolved. After explaining the reasons why it is not possible to attempt to determine causality using real world data without falling prey to a logical fallacy, this paper discusses a traditional approach used in science to deal with the problem. In particular, the paper argues that the appropriate approach for the problem is to build theories, with causality featuring as a part of those theories, and then to test those theories both for logical and empirical consistency.

  1. Quantum theory and local causality

    CERN Document Server

    Hofer-Szabó, Gábor

    2018-01-01

    This book summarizes the results of research the authors have pursued in the past years on the problem of implementing Bell's notion of local causality in local physical theories and relating it to other important concepts and principles in the foundations of physics such as the Common Cause Principle, Bell's inequalities, the EPR (Einstein-Podolsky-Rosen) scenario, and various other locality and causality concepts. The book is intended for philosophers of science with an interest in the formal background of sciences, philosophers of physics and physicists working in foundation of physics.

  2. Causality and complexity: the myth of objectivity in science.

    Science.gov (United States)

    Mikulecky, Donald C

    2007-10-01

    Two distinctly different worldviews dominate today's thinking in science and in the world of ideas outside of science. Using the approach advocated by Robert M. Hutchins, it is possible to see a pattern of interaction between ideas in science and in other spheres such as philosophy, religion, and politics. Instead of compartmentalizing these intellectual activities, it is worthwhile to look for common threads of mutual influence. Robert Rosen has created an approach to scientific epistemology that might seem radical to some. However, it has characteristics that resemble ideas in other fields, in particular in the writings of George Lakoff, Leo Strauss, and George Soros. Historically, the atmosphere at the University of Chicago during Hutchins' presidency gave rise to Rashevsky's relational biology, which Rosen carried forward. Strauss was writing his political philosophy there at the same time. One idea is paramount in all this, and it is Lakoff who gives us the most insight into how the worldviews differ using this idea. The central difference has to do with causality, the fundamental concept that we use to build a worldview. Causal entailment has two distinct forms in Lakoff 's analysis: direct causality and complex causality. Rosen's writings on complexity create a picture of complex causality that is extremely useful in its detail, grounding in the ideas of Aristotle. Strauss asks for a return to the ancients to put philosophy back on track. Lakoff sees the weaknesses in Western philosophy in a similar way, and Rosen provides tools for dealing with the problem. This introduction to the relationships between the thinking of these authors is meant to stimulate further discourse on the role of complex causal entailment in all areas of thought, and how it brings them together in a holistic worldview. The worldview built on complex causality is clearly distinct from that built around simple, direct causality. One important difference is that the impoverished causal

  3. Causal Reasoning with Mental Models

    Science.gov (United States)

    2014-08-08

    The initial rubric is equivalent to an exclusive disjunction between the two causal assertions. It 488 yields the following two mental models: 489...are 575 important, whereas the functions of artifacts are important (Ahn, 1998). A genetic code is 576 accordingly more critical to being a goat than

  4. Identity, causality, and pronoun ambiguity.

    Science.gov (United States)

    Sagi, Eyal; Rips, Lance J

    2014-10-01

    This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs. Copyright © 2014 Cognitive Science Society, Inc.

  5. Hyperactivity: is candy causal?

    Science.gov (United States)

    Krummel, D A; Seligson, F H; Guthrie, H A

    1996-01-01

    Adverse behavioral responses to ingestion of any kind of candy have been reported repeatedly in the lay press. Parents and teachers alike attribute excessive motor activity and other disruptive behaviors to candy consumption. However, anecdotal observations of this kind need to be tested scientifically before conclusions can be drawn, and criteria for interpreting diet behavior studies must be rigorous. Ingredients in nonchocolate candy (sugar, artificial food colors), components in chocolate candy (sugar, artificial food colors in coatings, caffeine), and chocolate itself have been investigated for any adverse effects on behavior. Feingold theorized that food additives (artificial colors and flavors) and natural salicylates caused hyperactivity in children and elimination of these components would result in dramatic improvement in behavior. Numerous double-blind studies of the Feingold hypothesis have led to the rejection of the idea that this elimination diet has any benefit beyond the normal placebo effect. Although sugar is widely believed by the public to cause hyperactive behavior, this has not been scientifically substantiated. Twelve double-blind, placebo-controlled studies of sugar challenges failed to provide any evidence that sugar ingestion leads to untoward behavior in children with Attention-Deficit Hyperactivity Disorder or in normal children. Likewise, none of the studies testing candy or chocolate found any negative effect of these foods on behavior. For children with behavioral problems, diet-oriented treatment does not appear to be appropriate. Rather, clinicians treating these children recommend a multidisciplinary approach. The goal of diet treatment is to ensure a balanced diet with adequate energy and nutrients for optimal growth.

  6. The causal boundary of wave-type spacetimes

    International Nuclear Information System (INIS)

    Flores, J.L.; Sanchez, M.

    2008-01-01

    A complete and systematic approach to compute the causal boundary of wave-type spacetimes is carried out. The case of a 1-dimensional boundary is specially analyzed and its critical appearance in pp-wave type spacetimes is emphasized. In particular, the corresponding results obtained in the framework of the AdS/CFT correspondence for holography on the boundary, are reinterpreted and very widely generalized. Technically, a recent new definition of causal boundary is used and stressed. Moreover, a set of mathematical tools is introduced (analytical functional approach, Sturm-Liouville theory, Fermat-type arrival time, Busemann-type functions)

  7. Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA

    Directory of Open Access Journals (Sweden)

    Chia-Lin Chang

    2017-10-01

    Full Text Available Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980 causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU, there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR test to a quasi-likelihood ratio test (QLR to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the

  8. Causality analysis of leading singular value decomposition modes identifies rotor as the dominant driving normal mode in fibrillation

    Science.gov (United States)

    Biton, Yaacov; Rabinovitch, Avinoam; Braunstein, Doron; Aviram, Ira; Campbell, Katherine; Mironov, Sergey; Herron, Todd; Jalife, José; Berenfeld, Omer

    2018-01-01

    Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K+ channel IKr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the IKr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the IKr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their

  9. Entanglement, holography and causal diamonds

    Science.gov (United States)

    de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

    2016-08-01

    We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

  10. Entanglement, holography and causal diamonds

    Energy Technology Data Exchange (ETDEWEB)

    Boer, Jan de [Institute of Physics, Universiteit van Amsterdam,Science Park 904, 1090 GL Amsterdam (Netherlands); Haehl, Felix M. [Centre for Particle Theory & Department of Mathematical Sciences, Durham University,South Road, Durham DH1 3LE (United Kingdom); Heller, Michal P.; Myers, Robert C. [Perimeter Institute for Theoretical Physics,31 Caroline Street North, Waterloo, Ontario N2L 2Y5 (Canada)

    2016-08-29

    We argue that the degrees of freedom in a d-dimensional CFT can be re-organized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglement entropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

  11. The argumentative impact of causal relations

    DEFF Research Database (Denmark)

    Nielsen, Anne Ellerup

    1996-01-01

    such as causality, explanation and justification. In certain types of discourse, causal relations also imply an intentional element. This paper describes the way in which the semantic and pragmatic functions of causal markers can be accounted for in terms of linguistic and rhetorical theories of argumentation.......The semantic relations between and within utterances are marked by the use of connectors and adverbials. One type of semantic relations is causal relations expressed by causal markers such as because, therefore, so, for, etc. Some of these markers cover different types of causal relations...

  12. Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

    Science.gov (United States)

    Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

    2012-09-01

    Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    Science.gov (United States)

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

  14. On causal nonrelativistic classical electrodynamics

    International Nuclear Information System (INIS)

    Goedecke, G.H.

    1984-01-01

    The differential-difference (DD) motion equations of the causal nonrelativistic classical electrodynamics developed by the author in 1975 are shown to possess only nonrunaway, causal solutions with no discontinuities in particle velocity or position. As an example, the DD equation solution for the problem of an electromagnetic shock incident on an initially stationary charged particle is contrasted with the standard Abraham-Lorentz equation solution. The general Cauchy problem for these DD motion equations is discussed. In general, in order to uniquely determine a solution, the initial data must be more detailed than the standard Cauchy data of initial position and velocity. Conditions are given under which the standard Cauchy data will determine the DD equation solutions to sufficient practical accuracy

  15. Quantum mechanics, relativity and causality

    International Nuclear Information System (INIS)

    Tati, Takao.

    1975-07-01

    In quantum mechanics, the state is prepared by a measurement on a space-like surface sigma. What is that determines the surface sigma on which the measurement prepares the state It is considered either a mechanism proper to the measuring process (apparatus) or a universal property of space-time. In the former case, problems arise, concerning causality or conservation of probability due to that the velocity of reduction of wave-packet is considered to exceed the light velocity. The theory of finite degree of freedom proposed previously belongs to the latter case. In this theory, the surface sigma is restricted to the hyper-plane perpendicular to a universal time-like vector governing causal relations. We propose an experiment to discriminate between the above-mentioned two cases and to test the existence of the universal time-like vector. (auth.)

  16. The causal relationship between energy consumption and economic growth in Lebanon

    International Nuclear Information System (INIS)

    Dagher, Leila; Yacoubian, Talar

    2012-01-01

    This paper investigates the dynamic causal relationship between energy consumption and economic growth in Lebanon over the period 1980–2009. Within a bivariate framework, imposed on us due to data limitations, and in an effort to increase the robustness of our results, we employ a variety of causality tests, namely, Hsiao, Toda-Yamamoto, and vector error correction based Granger causality tests. We find strong evidence of a bidirectional relationship both in the short-run and in the long-run, indicating that energy is a limiting factor to economic growth in Lebanon. From a policy perspective, the confirmation of the feedback hypothesis warns against the use of policy instruments geared towards restricting energy consumption, as these may lead to adverse effects on economic growth. Consequently, there is a pressing need to revise the current national energy policy that calls for a 5% energy conservation target. Also, to shield the country from external supply shocks, given its substantial dependence on energy imports, policymakers should emphasize the development of domestic energy resources. Further, the most pertinent implication is that relaxing the present electric capacity shortages should be made a national priority, in view of its potential positive effect on the economy. - Highlights: ► We investigate the energy-GDP nexus for Lebanon. ► Evidence of a bidirectional relationship both in the short- and the long-run is found. ► Reducing outages by expanding electric capacity should thus be prioritized. ► The energy plan calling for a 5% reduction in energy consumption needs to be revised. ► Development of domestic energy sources will help in mitigating energy supply shocks.

  17. DYNAMICS OF MUTUAL FUNDS IN RELATION TO STOCK MARKET: A VECTOR AUTOREGRESSIVE CAUSALITY ANALYSIS

    Directory of Open Access Journals (Sweden)

    Md. Shahadath Hossain

    2013-01-01

    Full Text Available In Bangladesh, primary and secondary mutual fund markets behave in a completely different way, where initial public offering (IPO investors of mutual funds earn more than 250 percent rerun, whereas secondary market investors cannot even manage to cover the opportunity cost of their investment. There are few other abnormalities present in this market – unlike everywhere in the world, most of the mutual funds are closed-end (92 percent and closed-end mutual funds are barred to issue bonus or right shares. A total of 714 day’s observations, from January 2008 to December 2010, of four variables– DSE (Dhaka Stock Exchange general index return, DSE general index turnover, mutual funds’ return and mutual funds’ turnover– are utilized. Stationarity of the variables are tested with Augmented Dickey-Fuller (ADF unit root test and found that variables are in different order of integration. Long-term equilibrium relationships among the variables are tested with Johansen cointegration and it is found that DSE general index return and mutual funds’ return are cointegrated. Toda-Yamamoto (TY version of granger non-causality test is employed and bidirectional causality is found moving from DSE (Dhaka Stock Exchange general index turnover to DSE general index return, whereas unidirectional causality is found moving from mutual fund’s return to DSE general index return, mutual funds’ return to mutual funds turnover, and DSE general index turnover to mutual funds turnover. This finding helps to conclude that equity shares’ demand drives the mutual funds demand but even higher demand of mutual funds fails to raise its own price unless underlying value of the mutual funds changes.

  18. Modeling of causality with metamaterials

    International Nuclear Information System (INIS)

    Smolyaninov, Igor I

    2013-01-01

    Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space–time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space–time. While this model may be used to build interesting space–time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space–time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler–Feynman absorber theory of causality. (paper)

  19. THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...

    Science.gov (United States)

    CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc

  20. Causal structure of analogue spacetimes

    International Nuclear Information System (INIS)

    Barcelo, Carlos; Liberati, Stefano; Sonego, Sebastiano; Visser, Matt

    2004-01-01

    The so-called 'analogue models of general relativity' provide a number of specific physical systems, well outside the traditional realm of general relativity, that nevertheless are well-described by the differential geometry of curved spacetime. Specifically, the propagation of perturbations in these condensed matter systems is described by 'effective metrics' that carry with them notions of 'causal structure' as determined by an exchange of quasi-particles. These quasi-particle-induced causal structures serve as specific examples of what can be done in the presence of a Lorentzian metric without having recourse to the Einstein equations of general relativity. (After all, the underlying analogue model is governed by its own specific physics, not necessarily by the Einstein equations.) In this paper we take a careful look at what can be said about the causal structure of analogue spacetimes, focusing on those containing quasi-particle horizons, both with a view to seeing what is different from standard general relativity, and what the similarities might be. For definiteness, and because the physics is particularly simple to understand, we will phrase much of the discussion in terms of acoustic disturbances in moving fluids, where the underlying physics is ordinary fluid mechanics, governed by the equations of traditional hydrodynamics, and the relevant quasi-particles are the phonons. It must however be emphasized that this choice of example is only for the sake of pedagogical simplicity and that our considerations apply generically to wide classes of analogue spacetimes

  1. Obesity and infection: reciprocal causality.

    Science.gov (United States)

    Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

    2015-01-01

    Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.

  2. Behavioural Pattern of Causality Parameter of Autoregressive ...

    African Journals Online (AJOL)

    In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...

  3. Exploring Individual Differences in Preschoolers' Causal Stance

    Science.gov (United States)

    Alvarez, Aubry; Booth, Amy E.

    2016-01-01

    Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In…

  4. Representing Personal Determinants in Causal Structures.

    Science.gov (United States)

    Bandura, Albert

    1984-01-01

    Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

  5. Causal inference in economics and marketing.

    Science.gov (United States)

    Varian, Hal R

    2016-07-05

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

  6. Causal knowledge and reasoning in decision making

    NARCIS (Netherlands)

    Hagmayer, Y.; Witteman, C.L.M.

    2017-01-01

    Normative causal decision theories argue that people should use their causal knowledge in decision making. Based on these ideas, we argue that causal knowledge and reasoning may support and thereby potentially improve decision making based on expected outcomes, narratives, and even cues. We will

  7. Emergent Geometry from Entropy and Causality

    Science.gov (United States)

    Engelhardt, Netta

    generalizations are discussed, both at the classical and perturbatively quantum limits. In particular, several No Go Theorems are proven, indicative of a conclusion that supplementary approaches or information may be necessary to recover the full spacetime geometry. Part II of this thesis involves the relation between geometry and causality, the property that information cannot travel faster than light. Requiring this of any quantum field theory results in constraints on string theory setups that are dual to quantum field theories via the AdS/CFT correspondence. At the level of perturbative quantum gravity, it is shown that causality in the field theory constraints the causal structure in the bulk. At the level of nonperturbative quantum string theory, we find that constraints on causal signals restrict the possible ways in which curvature singularities can be resolved in string theory. Finally, a new program of research is proposed for the construction of bulk geometry from the divergences of correlation functions in the dual field theory. This divergence structure is linked to the causal structure of the bulk and of the field theory.

  8. The Importance of Qualitative Research for Causal Explanation in Education

    Science.gov (United States)

    Maxwell, Joseph A.

    2012-01-01

    The concept of causation has long been controversial in qualitative research, and many qualitative researchers have rejected causal explanation as incompatible with an interpretivist or constructivist approach. This rejection conflates causation with the positivist "theory" of causation, and ignores an alternative understanding of causation,…

  9. Explanation in causal inference methods for mediation and interaction

    CERN Document Server

    VanderWeele, Tyler

    2015-01-01

    A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences.

  10. Quantum gravity from simplices: analytical investigations of causal dynamical triangulations

    NARCIS (Netherlands)

    Benedetti, D.

    2007-01-01

    A potentially powerful approach to quantum gravity has been developed over the last few years under the name of Causal Dynamical Triangulations. Although these models can be solved exactly in a variety of ways in the case of pure gravity in (1+1) dimensions,it is difficult to extend any of the

  11. A theory of causal learning in children: causal maps and Bayes nets.

    Science.gov (United States)

    Gopnik, Alison; Glymour, Clark; Sobel, David M; Schulz, Laura E; Kushnir, Tamar; Danks, David

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

  12. Causality and unitarity via the tree-loop duality relation

    Energy Technology Data Exchange (ETDEWEB)

    Tomboulis, E.T. [Mani L. Bhaumik Institute for Theoretical Physics,Department of Physics and Astronomy, UCLA,Los Angeles, CA 90095-1547 (United States)

    2017-05-29

    The tree-loop duality relation is used as a starting point to derive the constraints of causality and unitarity. Specifically, the Bogoliubov causality condition is ab initio derived at the individual graph level. It leads to a representation of a graph in terms of lower order cut graphs. Extracting the absorptive part gives then the general unitarity relation (Cutkosky rule). The derivation, being carried out directly in momentum space, holds for any local (polynomial) hermitian interaction vertices. This is in contrast to the technical difficulties arising from contact terms in the spacetime approach based on the largest time equation.

  13. Norms and customs: causally important or causally impotent?

    Science.gov (United States)

    Jones, Todd

    2010-01-01

    In this article, I argue that norms and customs, despite frequently being described as being causes of behavior in the social sciences and ordinary conversation, cannot really cause behavior. Terms like "norms" and the like seem to refer to philosophically disreputable disjunctive properties. More problematically, even if they do not, or even if there can be disjunctive properties after all, I argue that norms and customs still cannot cause behavior. The social sciences would be better off without referring to properties like norms and customs as if they could be causal.

  14. Impact of Energy Consumption on Economic Growth in Major OECD Economies (1977-2014): A Panel Data Approach

    OpenAIRE

    Aali-Bujari, Alí; Venegas-Martínez, Francisco; Palafox-Roca, Alfredo Omar

    2017-01-01

    This paper is aimed at assessing the impact of energy use growth on economic growth in the major economies of the Organization for Economic Cooperation and Development (OECD) during the period 1977-2014. To do this, a Granger causality analysis among relevant variables is carried out and, subsequently, a panel data model is estimated with the Generalized Method of Moments (GMM). The main empirical finding is that real GDP per capita growth is positively affected by the growth rate of energy u...

  15. The Use of Causal Mapping in the Design of Sustainability Performance Measurement Systems

    DEFF Research Database (Denmark)

    Parisi, Cristiana

    2013-01-01

    organisations’ strategic performance measurement systems (SPMSs). This study’s main contribution is the triangulation of multiple qualitative methods to enhance the reliability of causal maps. This innovative approach supports the use of causal mapping to extract managerial tacit knowledge in order to identify...

  16. Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

    Science.gov (United States)

    Gopnik, Alison; Wellman, Henry M.

    2012-01-01

    We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

  17. A theory of causal learning in children: Causal maps and Bayes nets

    OpenAIRE

    Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...

  18. Predicting the Cosmological Constant from the Causal Entropic Principle

    Energy Technology Data Exchange (ETDEWEB)

    Bousso, Raphael; Bousso, Raphael; Harnik, Roni; Kribs, Graham D.; Perez, Gilad

    2007-05-01

    We compute the expected value of the cosmological constant in our universe from the Causal Entropic Principle. Since observers must obey the laws of thermodynamics and causality, the principle asserts that physical parameters are most likely to be found in the range of values for which the total entropy production within a causally connected region is maximized. Despite the absence of more explicit anthropic criteria, the resulting probability distribution turns out to be in excellent agreement with observation. In particular, we find that dust heated by stars dominates the entropy production, demonstrating the remarkable power of this thermodynamic selection criterion. The alternative approach-weighting by the number of"observers per baryon" -- is less well-defined, requires problematic assumptions about the nature of observers, and yet prefers values larger than present experimental bounds.

  19. Predicting the Cosmological Constant from the CausalEntropic Principle

    Energy Technology Data Exchange (ETDEWEB)

    Bousso, Raphael; Harnik, Roni; Kribs, Graham D.; Perez, Gilad

    2007-02-20

    We compute the expected value of the cosmological constant in our universe from the Causal Entropic Principle. Since observers must obey the laws of thermodynamics and causality, it asserts that physical parameters are most likely to be found in the range of values for which the total entropy production within a causally connected region is maximized. Despite the absence of more explicit anthropic criteria, the resulting probability distribution turns out to be in excellent agreement with observation. In particular, we find that dust heated by stars dominates the entropy production, demonstrating the remarkable power of this thermodynamic selection criterion. The alternative approach--weighting by the number of ''observers per baryon''--is less well-defined, requires problematic assumptions about the nature of observers, and yet prefers values larger than present experimental bounds.

  20. Electricity consumption-real GDP causality nexus: Evidence from a bootstrapped causality test for 30 OECD countries

    International Nuclear Information System (INIS)

    Narayan, Paresh Kumar; Prasad, Arti

    2008-01-01

    The goal of this paper is to examine any causal effects between electricity consumption and real GDP for 30 OECD countries. We use a bootstrapped causality testing approach and unravel evidence in favour of electricity consumption causing real GDP in Australia, Iceland, Italy, the Slovak Republic, the Czech Republic, Korea, Portugal, and the UK. The implication is that electricity conservation policies will negatively impact real GDP in these countries. However, for the rest of the 22 countries our findings suggest that electricity conversation policies will not affect real GDP

  1. Causal mediation analysis with multiple causally non-ordered mediators.

    Science.gov (United States)

    Taguri, Masataka; Featherstone, John; Cheng, Jing

    2018-01-01

    In many health studies, researchers are interested in estimating the treatment effects on the outcome around and through an intermediate variable. Such causal mediation analyses aim to understand the mechanisms that explain the treatment effect. Although multiple mediators are often involved in real studies, most of the literature considered mediation analyses with one mediator at a time. In this article, we consider mediation analyses when there are causally non-ordered multiple mediators. Even if the mediators do not affect each other, the sum of two indirect effects through the two mediators considered separately may diverge from the joint natural indirect effect when there are additive interactions between the effects of the two mediators on the outcome. Therefore, we derive an equation for the joint natural indirect effect based on the individual mediation effects and their interactive effect, which helps us understand how the mediation effect works through the two mediators and relative contributions of the mediators and their interaction. We also discuss an extension for three mediators. The proposed method is illustrated using data from a randomized trial on the prevention of dental caries.

  2. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Directory of Open Access Journals (Sweden)

    Gerald eYoung

    2015-11-01

    Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

  3. Equity Theory Ratios as Causal Schemas

    Directory of Open Access Journals (Sweden)

    Alexios Arvanitis

    2016-08-01

    Full Text Available Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

  4. Equity Theory Ratios as Causal Schemas.

    Science.gov (United States)

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

  5. The Functions of Danish Causal Conjunctions

    Directory of Open Access Journals (Sweden)

    Rita Therkelsen

    2004-01-01

    Full Text Available In the article I propose an analysis of the Danish causal conjunctions fordi, siden and for based on the framework of Danish Functional Grammar. As conjunctions they relate two clauses, and their semantics have in common that it indicates a causal relationship between the clauses. The causal conjunctions are different as far as their distribution is concerned; siden conjoins a subordinate clause and a main clause, for conjoins two main clauses, and fordi is able to do both. Methodologically I have based my analysis on these distributional properties comparing siden and fordi conjoining a subordinate and a main clause, and comparing for and fordi conjoining two main clauses, following the thesis that they would establish a causal relationship between different kinds of content. My main findings are that fordi establishes a causal relationship between the events referred to by the two clauses, and the whole utterance functions as a statement of this causal relationship. Siden presupposes such a general causal relationship between the two events and puts forward the causing event as a reason for assuming or wishing or ordering the caused event, siden thus establishes a causal relationship between an event and a speech act. For equally presupposes a general causal relationship between two events and it establishes a causal relationship between speech acts, and fordi conjoining two main clauses is able to do this too, but in this position it also maintains its event-relating ability, the interpretation depending on contextual factors.

  6. Space and time in perceptual causality

    Directory of Open Access Journals (Sweden)

    Benjamin Straube

    2010-04-01

    Full Text Available Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte’s view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks.

  7. Causal diagrams in systems epidemiology

    Directory of Open Access Journals (Sweden)

    Joffe Michael

    2012-03-01

    Full Text Available Abstract Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s. Transmitted causes ("causes of causes" tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.

  8. Causal diagrams in systems epidemiology.

    Science.gov (United States)

    Joffe, Michael; Gambhir, Manoj; Chadeau-Hyam, Marc; Vineis, Paolo

    2012-03-19

    Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.

  9. Probabilistic causality and radiogenic cancers

    International Nuclear Information System (INIS)

    Groeer, P.G.

    1986-01-01

    A review and scrutiny of the literature on probability and probabilistic causality shows that it is possible under certain assumptions to estimate the probability that a certain type of cancer diagnosed in an individual exposed to radiation prior to diagnosis was caused by this exposure. Diagnosis of this causal relationship like diagnosis of any disease - malignant or not - requires always some subjective judgments by the diagnostician. It is, therefore, illusory to believe that tables based on actuarial data can provide objective estimates of the chance that a cancer diagnosed in an individual is radiogenic. It is argued that such tables can only provide a base from which the diagnostician(s) deviate in one direction or the other according to his (their) individual (consensual) judgment. Acceptance of a physician's diagnostic judgment by patients is commonplace. Similar widespread acceptance of expert judgment by claimants in radiation compensation cases does presently not exist. Judicious use of the present radioepidemiological tables prepared by the Working Group of the National Institutes of Health or of updated future versions of similar tables may improve the situation. 20 references

  10. Inferring causal molecular networks: empirical assessment through a community-based effort.

    Science.gov (United States)

    Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

    2016-04-01

    It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

  11. The discourse of causal explanations in school science

    Science.gov (United States)

    Slater, Tammy Jayne Anne

    Researchers and educators working from a systemic functional linguistic perspective have provided a body of work on science discourse which offers an excellent starting point for examining the linguistic aspects of the development of causal discourse in school science, discourse which Derewianka (1995) claimed is critical to success in secondary school. No work has yet described the development of causal language by identifying the linguistic features present in oral discourse or by comparing the causal discourse of native and non-native (ESL) speakers of English. The current research responds to this gap by examining the oral discourse collected from ESL and non-ESL students at the primary and high school grades. Specifically, it asks the following questions: (1) How do the teachers and students in these four contexts develop causal explanations and their relevant taxonomies through classroom interactions? (2) What are the causal discourse features being used by the students in these four contexts to construct oral causal explanations? The findings of the social practice analysis showed that the teachers in the four contexts differed in their approaches to teaching, with the primary school mainstream teacher focusing largely on the hands-on practice , the primary school ESL teacher moving from practice to theory, the high school mainstream teacher moving from theory to practice, and the high school ESL teacher relying primarily on theory. The findings from the quantitative, small corpus approach suggest that the developmental path of cause which has been identified in the writing of experts shows up not only in written texts but also in the oral texts which learners construct. Moreover, this move appears when the discourse of high school ESL and non-ESL students is compared, suggesting a developmental progression in the acquisition of these features by these students. The findings also reveal that the knowledge constructed, as shown by the concept maps created

  12. An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

    KAUST Repository

    Zenil, Hector

    2017-09-08

    We introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system\\'s actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system\\'s dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system\\'s intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.

  13. Time and frequency structure of causal correlation networks in the China bond market

    Science.gov (United States)

    Wang, Zhongxing; Yan, Yan; Chen, Xiaosong

    2017-07-01

    There are more than eight hundred interest rates published in the China bond market every day. Identifying the benchmark interest rates that have broad influences on most other interest rates is a major concern for economists. In this paper, a multi-variable Granger causality test is developed and applied to construct a directed network of interest rates, whose important nodes, regarded as key interest rates, are evaluated with CheiRank scores. The results indicate that repo rates are the benchmark of short-term rates, the central bank bill rates are in the core position of mid-term interest rates network, and treasury bond rates lead the long-term bond rates. The evolution of benchmark interest rates from 2008 to 2014 is also studied, and it is found that SHIBOR has generally become the benchmark interest rate in China. In the frequency domain we identify the properties of information flows between interest rates, and the result confirms the existence of market segmentation in the China bond market.

  14. Linear causal modeling with structural equations

    CERN Document Server

    Mulaik, Stanley A

    2009-01-01

    Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

  15. Causal relationship: a new tool for the causal characterization of Lorentzian manifolds

    International Nuclear Information System (INIS)

    Garcia-Parrado, Alfonso; Senovilla, Jose M M

    2003-01-01

    We define and study a new kind of relation between two diffeomorphic Lorentzian manifolds called a causal relation, which is any diffeomorphism characterized by mapping every causal vector of the first manifold onto a causal vector of the second. We perform a thorough study of the mathematical properties of causal relations and prove in particular that two given Lorentzian manifolds (say V and W) may be causally related only in one direction (say from V to W, but not from W to V). This leads us to the concept of causally equivalent (or isocausal in short) Lorentzian manifolds as those mutually causally related and to a definition of causal structure over a differentiable manifold as the equivalence class formed by isocausal Lorentzian metrics upon it. Isocausality is a more general concept than the conformal relationship, because we prove the remarkable result that a conformal relation φ is characterized by the fact of being a causal relation of the particular kind in which both φ and φ -1 are causal relations. Isocausal Lorentzian manifolds are mutually causally compatible, they share some important causal properties, and there are one-to-one correspondences, which are sometimes non-trivial, between several classes of their respective future (and past) objects. A more important feature is that they satisfy the same standard causality constraints. We also introduce a partial order for the equivalence classes of isocausal Lorentzian manifolds providing a classification of all the causal structures that a given fixed manifold can have. By introducing the concept of causal extension we put forward a new definition of causal boundary for Lorentzian manifolds based on the concept of isocausality, and thereby we generalize the traditional Penrose constructions of conformal infinity, diagrams and embeddings. In particular, the concept of causal diagram is given. Many explicit clarifying examples are presented throughout the paper

  16. Space-time as a causal set

    International Nuclear Information System (INIS)

    Bombelli, L.; Lee, J.; Meyer, D.; Sorkin, R.D.

    1987-01-01

    We propose that space-time at the smallest scales is in reality a causal set: a locally finite set of elements endowed with a partial order corresponding to the macroscopic relation that defines past and future. We explore how a Lorentzian manifold can approximate a causal set, noting in particular that the thereby defined effective dimensionality of a given causal set can vary with length scale. Finally, we speculate briefly on the quantum dynamics of causal sets, indicating why an appropriate choice of action can reproduce general relativity in the classical limit

  17. Amodal causal capture in the tunnel effect.

    Science.gov (United States)

    Bae, Gi Yeul; Flombaum, Jonathan I

    2011-01-01

    In addition to identifying individual objects in the world, the visual system must also characterize the relationships between objects, for instance when objects occlude one another or cause one another to move. Here we explored the relationship between perceived causality and occlusion. Can one perceive causality in an occluded location? In several experiments, observers judged whether a centrally presented event involved a single object passing behind an occluder, or one object causally launching another (out of view and behind the occluder). With no additional context, the centrally presented event was typically judged as a non-causal pass, even when the occluding and disoccluding objects were different colors--an illusion known as the 'tunnel effect' that results from spatiotemporal continuity. However, when a synchronized context event involved an unambiguous causal launch, participants perceived a causal launch behind the occluder. This percept of an occluded causal interaction could also be driven by grouping and synchrony cues in the absence of any explicitly causal interaction. These results reinforce the hypothesis that causality is an aspect of perception. It is among the interpretations of the world that are independently available to vision when resolving ambiguity, and that the visual system can 'fill in' amodally.

  18. Electromagnetic pulses, localized and causal

    Science.gov (United States)

    Lekner, John

    2018-01-01

    We show that pulse solutions of the wave equation can be expressed as time Fourier superpositions of scalar monochromatic beam wave functions (solutions of the Helmholtz equation). This formulation is shown to be equivalent to Bateman's integral expression for solutions of the wave equation, for axially symmetric solutions. A closed-form one-parameter solution of the wave equation, containing no backward-propagating parts, is constructed from a beam which is the tight-focus limit of two families of beams. Application is made to transverse electric and transverse magnetic pulses, with evaluation of the energy, momentum and angular momentum for a pulse based on the general localized and causal form. Such pulses can be represented as superpositions of photons. Explicit total energy and total momentum values are given for the one-parameter closed-form pulse.

  19. Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

    International Nuclear Information System (INIS)

    Neelamkavil, Raphael

    2014-01-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

  20. Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

    Energy Technology Data Exchange (ETDEWEB)

    Neelamkavil, Raphael

    2014-07-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

  1. Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach

    International Nuclear Information System (INIS)

    Fan, Ying; Wei, Yi-Ming; Zhang, Yue-Jun; Tsai, Hsien-Tang

    2008-01-01

    Estimation has been carried out using GARCH-type models, based on the Generalized Error Distribution (GED), for both the extreme downside and upside Value-at-Risks (VaR) of returns in the WTI and Brent crude oil spot markets. Furthermore, according to a new concept of Granger causality in risk, a kernel-based test is proposed to detect extreme risk spillover effect between the two oil markets. Results of an empirical study indicate that the GED-GARCH-based VaR approach appears more effective than the well-recognized HSAF (i.e. historical simulation with ARMA forecasts). Moreover, this approach is also more realistic and comprehensive than the standard normal distribution-based VaR model that is commonly used. Results reveal that there is significant two-way risk spillover effect between WTI and Brent markets. Supplementary study indicates that at the 99% confidence level, when negative market news arises that brings about a slump in oil price return, historical information on risk in the WTI market helps to forecast the Brent market. Conversely, it is not the case when positive news occurs and returns rise. Historical information on risk in the two markets can facilitate forecasts of future extreme market risks for each other. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in international crude oil markets. (author)

  2. Causality from the Cosmological Perspective in Vedanta and Western Physics.

    Science.gov (United States)

    Hawley, Danny Lee

    The relation between Western physics and Indian Vedanta philosophy is investigated through the topic of causality, taken in the sense of explanatory theories of the origin of the universe and the relations among its physical, mental, and spiritual aspects. Both physics and Vedanta have a common goal of explanation by means of a unitary principle. While physics has long been separated from metaphysics, its discoveries indicate that consciousness must be included in a complete explanation. Consciousness is taken as the fundamental basis and source of all phenomena in Vedanta. This work traces the developments of causal explanation in Western physics and Indian philosophy, and considers how these views may relate to each other and how they may together suggest a comprehensive view of reality. Approaches typically applied by historians of religion to the study of creation myths, especially the psychological approach which considers myths from the perspective or cyclical stages of conscious development, are applied to the causal theories of the two cultures. The question of how causal explanations attempt to bridge the gap between cause and effect, unity and multiplicity, absolute and relative, conscious and unconscious, etc., is addressed. Though the investigation begins from the earliest causal explanations, viz., creation myths, emphasis is placed upon Samkara's commentaries of Advaita Vedanta, examined in the original Sanskrit, and upon the convergence of modern field theory, astrophysics, and cosmology, seen from the perspective of a previous doctorate in physics. Consideration is given to the comparison between physics and Vedanta as to goals, methods, and domains, to the question of the incompleteness of physics and the extent to which it nevertheless points beyond itself, to the possibility of a synthetic view and how it might be effected, and to analogies and metaphors through which physics and Vedanta may illuminate each other. An intuitive picture is

  3. A Theory of Causal Learning in Children: Causal Maps and Bayes Nets

    Science.gov (United States)

    Gopnik, Alison; Glymour, Clark; Sobel, David M.; Schulz, Laura E.; Kushnir, Tamar; Danks, David

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously…

  4. Further properties of causal relationship: causal structure stability, new criteria for isocausality and counterexamples

    International Nuclear Information System (INIS)

    Garcia-Parrado, Alfonso; Sanchez, Miguel

    2005-01-01

    Recently (Garcia-Parrado and Senovilla 2003 Class. Quantum Grav. 20 625-64) the concept of causal mapping between spacetimes, essentially equivalent in this context to the chronological map defined in abstract chronological spaces, and the related notion of causal structure, have been introduced as new tools to study causality in Lorentzian geometry. In the present paper, these tools are further developed in several directions such as (i) causal mappings-and, thus, abstract chronological ones-do not preserve two levels of the standard hierarchy of causality conditions (however, they preserve the remaining levels as shown in the above reference), (ii) even though global hyperbolicity is a stable property (in the set of all time-oriented Lorentzian metrics on a fixed manifold), the causal structure of a globally hyperbolic spacetime can be unstable against perturbations; in fact, we show that the causal structures of Minkowski and Einstein static spacetimes remain stable, whereas that of de Sitter becomes unstable, (iii) general criteria allow us to discriminate different causal structures in some general spacetimes (e.g. globally hyperbolic, stationary standard); in particular, there are infinitely many different globally hyperbolic causal structures (and thus, different conformal ones) on R 2 (iv) plane waves with the same number of positive eigenvalues in the frequency matrix share the same causal structure and, thus, they have equal causal extensions and causal boundaries

  5. Applying causal mediation analysis to personality disorder research.

    Science.gov (United States)

    Walters, Glenn D

    2018-01-01

    This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  6. Computation of Probabilities in Causal Models of History of Science

    Directory of Open Access Journals (Sweden)

    Osvaldo Pessoa Jr.

    2006-12-01

    Full Text Available : The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations. Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY=X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY=X(t, which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances.

  7. The continuum limit of causal fermion systems from Planck scale structures to macroscopic physics

    CERN Document Server

    Finster, Felix

    2016-01-01

    This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries". The dynamics is described by a novel variational principle, called the causal action principle. In addition to the basics, the book provides all the necessary mathematical background and explains how the causal action principle gives rise to the interactions of the standard model plus gravity on the level of second-quantized fermionic fields coupled to classical bosonic fields. The focus is on getting a mathematically sound connection between causal fermion systems and physical systems in Minkowski space. The book is intended for graduate students e...

  8. Causal Mediation Analysis: Warning! Assumptions Ahead

    Science.gov (United States)

    Keele, Luke

    2015-01-01

    In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

  9. A Causal Model of Faculty Research Productivity.

    Science.gov (United States)

    Bean, John P.

    A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…

  10. Causal Indicators Can Help to Interpret Factors

    Science.gov (United States)

    Bentler, Peter M.

    2016-01-01

    The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

  11. Quasi-Experimental Designs for Causal Inference

    Science.gov (United States)

    Kim, Yongnam; Steiner, Peter

    2016-01-01

    When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

  12. Determining Directional Dependency in Causal Associations

    Science.gov (United States)

    Pornprasertmanit, Sunthud; Little, Todd D.

    2012-01-01

    Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of…

  13. Heterogeneous Causal Effects and Sample Selection Bias

    DEFF Research Database (Denmark)

    Breen, Richard; Choi, Seongsoo; Holm, Anders

    2015-01-01

    The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how...... causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects....... This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias...

  14. Causality, spin, and equal-time commutators

    International Nuclear Information System (INIS)

    Abdel-Rahman, A.M.

    1975-01-01

    We study the causality constraints on the structure of the Lorentz-antisymmetric component of the commutator of two conserved isovector currents between fermion states of equal momenta. We discuss the sum rules that follow from causality and scaling, using the recently introduced refined infinite-momentum technique. The complete set of sum rules is found to include the spin-dependent fixed-mass sum rules obtained from light-cone commutators. The causality and scaling restrictions on the structure of the electromagnetic equal-time commutators are discussed, and it is found, in particular, that causality requires the spin-dependent part of the matrix element for the time-space electromagnetic equal-time commutator to vanish identically. It is also shown, in comparison with the electromagnetic case, that the corresponding matrix element for the time-space isovector current equal-time commutator is required, by causality, to have isospin-antisymmetric tensor and scalar operator Schwinger terms

  15. Speeding Up Maximal Causality Reduction with Static Dependency Analysis

    OpenAIRE

    Huang, Shiyou; Huang, Jeff

    2017-01-01

    Stateless Model Checking (SMC) offers a powerful approach to verifying multithreaded programs but suffers from the state-space explosion problem caused by the huge thread interleaving space. The pioneering reduction technique Partial Order Reduction (POR) mitigates this problem by pruning equivalent interleavings from the state space. However, limited by the happens-before relation, POR still explores redundant executions. The recent advance, Maximal Causality Reduction (MCR), shows a promisi...

  16. Female Labor Supply and Fertility. Causal Evidence for Latin America

    OpenAIRE

    Darío Tortarolo

    2014-01-01

    In this paper I study the causal relationship between fertility and female labor supply using census data from 14 Latin American countries and the U.S. over the span of three decades (1980, 1990 and 2000). Parental preferences for a gender-balanced family (mixed-sex children) is exploited as a source of exogenous variation in fertility. Although OLS estimates suggest a statistically signi cant negative relationship in the 39 censuses used, instrumental variables approach fails to identify a c...

  17. Causal mediation analysis for longitudinal data with exogenous exposure

    OpenAIRE

    Bind, M.-A. C.; Vanderweele, T. J.; Coull, B. A.; Schwartz, J. D.

    2015-01-01

    Mediation analysis is a valuable approach to examine pathways in epidemiological research. Prospective cohort studies are often conducted to study biological mechanisms and often collect longitudinal measurements on each participant. Mediation formulae for longitudinal data have been developed. Here, we formalize the natural direct and indirect effects using a causal framework with potential outcomes that allows for an interaction between the exposure and the mediator. To allow different type...

  18. An integrated specification for the nexus of water pollution and economic growth in China: Panel cointegration, long-run causality and environmental Kuznets curve.

    Science.gov (United States)

    Zhang, Chen; Wang, Yuan; Song, Xiaowei; Kubota, Jumpei; He, Yanmin; Tojo, Junji; Zhu, Xiaodong

    2017-12-31

    This paper concentrates on a Chinese context and makes efforts to develop an integrated process to explicitly elucidate the relationship between economic growth and water pollution discharge-chemical oxygen demand (COD) discharge and ammonia nitrogen (NH 3 -N), using two unbalanced panel data sets covering the period separately from 1990 to 2014, and 2001 to 2014. In our present study, the panel unit root tests, cointegration tests, and Granger causality tests allowing for cross-sectional dependence, nonstationary, and heterogeneity are conducted to examine the causal effects of economic growth on COD/NH 3 -N discharge. Further, we simultaneously apply semi-parametric fixed effects estimation and parametric fixed effects estimation to investigate environmental Kuznets curve relationship for COD/NH 3 -N discharge. Our empirical results show a long-term bidirectional causality between economic growth and COD/NH 3 -N discharge in China. Within the Stochastic Impacts by Regression on Population, Affluence and Technology framework, we find evidence in support of an inverted U-shaped curved link between economic growth and COD/NH 3 -N discharge. To the best of our knowledge, there have not been any efforts made in investigating the nexus of economic growth and water pollution in such an integrated manner. Therefore, this study takes a fresh look on this topic. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Causal ubiquity in quantum physics a superluminal and local-causal physical ontology

    CERN Document Server

    Neelamkavil, Raphael

    2014-01-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly non-causal processes, something exists processually in extension-motion, between the causal and the non-causal. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That

  20. Causal reasoning and models of cognitive tasks for naval nuclear power plant operators

    International Nuclear Information System (INIS)

    Salazar-Ferrer, P.

    1995-06-01

    In complex industrial process control, causal reasoning appears as a major component in operators' cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs

  1. Causality violations in Lovelock theories

    Science.gov (United States)

    Brustein, Ram; Sherf, Yotam

    2018-04-01

    Higher-derivative gravity theories, such as Lovelock theories, generalize Einstein's general relativity (GR). Modifications to GR are expected when curvatures are near Planckian and appear in string theory or supergravity. But can such theories describe gravity on length scales much larger than the Planck cutoff length scale? Here we find causality constraints on Lovelock theories that arise from the requirement that the equations of motion (EOM) of perturbations be hyperbolic. We find a general expression for the "effective metric" in field space when Lovelock theories are perturbed around some symmetric background solution. In particular, we calculate explicitly the effective metric for a general Lovelock theory perturbed around cosmological Friedman-Robertson-Walker backgrounds and for some specific cases when perturbed around Schwarzschild-like solutions. For the EOM to be hyperbolic, the effective metric needs to be Lorentzian. We find that, unlike for GR, the effective metric is generically not Lorentzian when the Lovelock modifications are significant. So, we conclude that Lovelock theories can only be considered as perturbative extensions of GR and not as truly modified theories of gravity. We compare our results to those in the literature and find that they agree with and reproduce the results of previous studies.

  2. Causal models in epidemiology: past inheritance and genetic future

    Directory of Open Access Journals (Sweden)

    Kriebel David

    2006-07-01

    Full Text Available Abstract The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional

  3. Causal localizations in relativistic quantum mechanics

    Science.gov (United States)

    Castrigiano, Domenico P. L.; Leiseifer, Andreas D.

    2015-07-01

    Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac's localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.

  4. Causal inference, probability theory, and graphical insights.

    Science.gov (United States)

    Baker, Stuart G

    2013-11-10

    Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.

  5. Carbon emissions, energy consumption and output: A threshold analysis on the causal dynamics in emerging African economies

    International Nuclear Information System (INIS)

    Mensah, Justice Tei

    2014-01-01

    Following the recent global economic downturn, attention has gradually shifted towards emerging economies which have experienced robust growth amidst sluggish growth of the world economy. A significant number of these emerging economies are in Africa. Rising growth in these economies is associated with surging demand for energy to propel the engines of growth, with direct implications on emissions into the atmosphere. Further, these economies are constantly being shaped by series of structural reforms with direct and indirect effects on growth, demand for energy, etc. To this end, this paper examines the causal dynamics among energy use, real GDP and CO 2 emissions in the presence of regime shifts in six emerging African economies using the Gregory and Hansen (1996a). J. Econ. 70, 99–126 threshold cointegration and the Toda and Yamamoto (1995). J. Econometrics. 66, 225–250 Granger causality techniques. Results confirm the presence of regime shift effects in the long run inter-linkages among energy use, real GDP and CO 2 emissions in the countries considered, thus indicating that structural changes have both economic and environmental effects. Hence, integration of energy and environmental policies into development plans is imperative towards attaining sustainable growth and development. - Highlights: • The paper examines the causal dynamics among output, energy demand and carbon emissions in the presence of regime shifts. • Regime shift have significant effects on the nexus among energy use, real GDP and CO 2 emissions. • Results suggest that structural changes in selected countries have both economic and environmental effects. • Integration of energy and environmental policies into development plans is desirable

  6. Dual Causality and the Autonomy of Biology.

    Science.gov (United States)

    Bock, Walter J

    2017-03-01

    Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

  7. Mathematical implications of Einstein-Weyl causality

    International Nuclear Information System (INIS)

    Borchers, H.J.; Sen, R.N.

    2006-01-01

    The present work is the first systematic attempt at answering the following fundamental question: what mathematical structures does Einstein-Weyl causality impose on a point-set that has no other previous structure defined on it? The authors propose an axiomatization of Einstein-Weyl causality (inspired by physics), and investigate the topological and uniform structures that it implies. Their final result is that a causal space is densely embedded in one that is locally a differentiable manifold. The mathematical level required of the reader is that of the graduate student in mathematical physics. (orig.)

  8. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  9. From causal dynamical triangulations to astronomical observations

    Science.gov (United States)

    Mielczarek, Jakub

    2017-09-01

    This letter discusses phenomenological aspects of dimensional reduction predicted by the Causal Dynamical Triangulations (CDT) approach to quantum gravity. The deformed form of the dispersion relation for the fields defined on the CDT space-time is reconstructed. Using the Fermi satellite observations of the GRB 090510 source we find that the energy scale of the dimensional reduction is E* > 0.7 \\sqrt{4-d\\text{UV}} \\cdot 1010 \\text{GeV} at (95% CL), where d\\text{UV} is the value of the spectral dimension in the UV limit. By applying the deformed dispersion relation to the cosmological perturbations it is shown that, for a scenario when the primordial perturbations are formed in the UV region, the scalar power spectrum PS \\propto kn_S-1 , where n_S-1≈ \\frac{3 r (d\\text{UV}-2)}{(d\\text{UV}-1)r-48} . Here, r is the tensor-to-scalar ratio. We find that within the considered model, the predicted from CDT deviation from the scale invariance (n_S=1) is in contradiction with the up to date Planck and BICEP2.

  10. Dynamic Causal Models and Autopoietic Systems

    Directory of Open Access Journals (Sweden)

    OLIVIER DAVID

    2007-01-01

    Full Text Available Dynamic Causal Modelling (DCM and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated

  11. Dilepton production in schematic causal viscous hydrodynamics

    International Nuclear Information System (INIS)

    Song, Taesoo; Han, Kyong Chol; Ko, Che Ming

    2011-01-01

    Assuming that in the hot dense matter produced in relativistic heavy-ion collisions, the energy density, entropy density, and pressure as well as the azimuthal and space-time rapidity components of the shear tensor are uniform in the direction transversal to the reaction plane, we derive a set of schematic equations from the Isreal-Stewart causal viscous hydrodynamics. These equations are then used to describe the evolution dynamics of relativistic heavy-ion collisions by taking the shear viscosity to entropy density ratio of 1/4π for the initial quark-gluon plasma (QGP) phase and of 10 times this value for the later hadron-gas (HG) phase. Using the production rate evaluated with particle distributions that take into account the viscous effect, we study dilepton production in central heavy-ion collisions. Compared with results from the ideal hydrodynamics, we find that although the dilepton invariant mass spectra from the two approaches are similar, the transverse momentum spectra are significantly enhanced at high transverse momenta by the viscous effect. We also study the transverse momentum dependence of dileptons produced from QGP for a fixed transverse mass, which is essentially absent in the ideal hydrodynamics, and find that this so-called transverse mass scaling is violated in the viscous hydrodynamics, particularly at high transverse momenta.

  12. Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

    Science.gov (United States)

    Matiasz, Nicholas J; Wood, Justin; Wang, Wei; Silva, Alcino J; Hsu, William

    2017-01-01

    Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.

  13. The role of causal maps in intellectual capital measurement and management

    DEFF Research Database (Denmark)

    Montemari, Marco; Nielsen, Christian

    2013-01-01

    Purpose – The purpose of this paper is to investigate the measurement and the management of the dynamic aspects of intellectual capital through the use of causal mapping. Design/methodology/approach – The study details the methods utilized in a single in-depth case study of a network-based business...... of the lag and the persistence of the effects of managerial actions. In addition, it can signal when and how to refine and update the causal map. The combination of these factors supports the dynamic measurement and management of intellectual capital. Research limitations/implications – The paper presented...... the causal mapping approach into practice. Practical implications – The paper highlights the need to build causal maps to enhance the measurement and management of intellectual capital, which is dynamic of nature. As a consequence, this tool can be useful for companies to monitor their intangibles...

  14. Altered cortical causality after remifentanil administration in healthy volunteers

    DEFF Research Database (Denmark)

    Khodayari-Rostamabad, Ahmad; Graversen, Carina; Olesen, Soren S

    2014-01-01

    and after infusion of remifentanil and placebo. Additionally, to assess cognitive function and analgesic effect, continuous reaction time (CRT) and bone pressure and heat pain were assessed, respectively. The causality information was extracted from the EEG by phase slope index (PSI). Among the features...... being reproducible between the two baseline recordings, several PSI features were altered by remifentanil administration in comparison to placebo. Furthermore, several of the PSI features altered by remifentanil were correlated to changes in both CRT and pain scores. The results indicate...... that remifentanil administration influence the information flow between several brain areas. Hence, the EEG causality approach offers the potential to assist in deciphering the cortical effects of remifentanil administration....

  15. Assessing students' beliefs, emotions and causal attribution ...

    African Journals Online (AJOL)

    Keywords: academic emotion; belief; causal attribution; statistical validation; students' conceptions of learning ... Sadi & Lee, 2015), through their effect on motivation and learning strategies .... to understand why they may or may not be doing.

  16. Selecting appropriate cases when tracing causal mechanisms

    DEFF Research Database (Denmark)

    Beach, Derek; Pedersen, Rasmus Brun

    2016-01-01

    The last decade has witnessed resurgence in the interest in studying the causal mechanisms linking causes and outcomes in the social sciences. This article explores the overlooked implications for case selection when tracing mechanisms using in-depth case studies. Our argument is that existing case...... selection guidelines are appropriate for research aimed at making cross-case claims about causal relationships, where case selection is primarily used to control for other causes. However, existing guidelines are not in alignment with case-based research that aims to trace mechanisms, where the goal...... is to unpack the causal mechanism between X and Y, enabling causal inferences to be made because empirical evidence is provided for how the mechanism actually operated in a particular case. The in-depth, within-case tracing of how mechanisms operate in particular cases produces what can be termed mechanistic...

  17. Rate-Agnostic (Causal) Structure Learning.

    Science.gov (United States)

    Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

    2015-12-01

    Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.

  18. K-causality and degenerate spacetimes

    Science.gov (United States)

    Dowker, H. F.; Garcia, R. S.; Surya, S.

    2000-11-01

    The causal relation K+ was introduced by Sorkin and Woolgar to extend the standard causal analysis of C2 spacetimes to those that are only C0. Most of their results also hold true in the case of metrics with degeneracies which are C0 but vanish at isolated points. In this paper we seek to examine K+ explicitly in the case of topology-changing `Morse histories' which contain degeneracies. We first demonstrate some interesting features of this relation in globally Lorentzian spacetimes. In particular, we show that K+ is robust and the Hawking and Sachs characterization of causal continuity translates into a natural condition in terms of K+. We then examine K+ in topology-changing Morse spacetimes with the degenerate points excised and then for the Morse histories in which the degenerate points are reinstated. We find further characterizations of causal continuity in these cases.

  19. A possible realization of Einstein's causal theory underlying quantum mechanics

    International Nuclear Information System (INIS)

    Yussouff, M.

    1979-06-01

    It is shown that a new microscopic mechanics formulated earlier can be looked upon as a possible causal theory underlying quantum mechanics, which removes Einstein's famous objections against quantum theory. This approach is free from objections raised against Bohm's hidden variable theory and leads to a clear physical picture in terms of familiar concepts, if self interactions are held responsible for deviations from classical behaviour. The new level of physics unfolded by this approach may reveal novel frontiers in high-energy physics. (author)

  20. Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

    OpenAIRE

    Gopnik, Alison; Wellman, Henry M.

    2012-01-01

    We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...

  1. Efficient nonparametric estimation of causal mediation effects

    OpenAIRE

    Chan, K. C. G.; Imai, K.; Yam, S. C. P.; Zhang, Z.

    2016-01-01

    An essential goal of program evaluation and scientific research is the investigation of causal mechanisms. Over the past several decades, causal mediation analysis has been used in medical and social sciences to decompose the treatment effect into the natural direct and indirect effects. However, all of the existing mediation analysis methods rely on parametric modeling assumptions in one way or another, typically requiring researchers to specify multiple regression models involving the treat...

  2. Inference of Boundaries in Causal Sets

    OpenAIRE

    Cunningham, William

    2017-01-01

    We investigate the extrinsic geometry of causal sets in $(1+1)$-dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons-Hawking-York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space...

  3. Causal strength induction from time series data.

    Science.gov (United States)

    Soo, Kevin W; Rottman, Benjamin M

    2018-04-01

    One challenge when inferring the strength of cause-effect relations from time series data is that the cause and/or effect can exhibit temporal trends. If temporal trends are not accounted for, a learner could infer that a causal relation exists when it does not, or even infer that there is a positive causal relation when the relation is negative, or vice versa. We propose that learners use a simple heuristic to control for temporal trends-that they focus not on the states of the cause and effect at a given instant, but on how the cause and effect change from one observation to the next, which we call transitions. Six experiments were conducted to understand how people infer causal strength from time series data. We found that participants indeed use transitions in addition to states, which helps them to reach more accurate causal judgments (Experiments 1A and 1B). Participants use transitions more when the stimuli are presented in a naturalistic visual format than a numerical format (Experiment 2), and the effect of transitions is not driven by primacy or recency effects (Experiment 3). Finally, we found that participants primarily use the direction in which variables change rather than the magnitude of the change for estimating causal strength (Experiments 4 and 5). Collectively, these studies provide evidence that people often use a simple yet effective heuristic for inferring causal strength from time series data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  4. Kant on causal laws and powers.

    Science.gov (United States)

    Henschen, Tobias

    2014-12-01

    The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

  5. Causality violation, gravitational shockwaves and UV completion

    Energy Technology Data Exchange (ETDEWEB)

    Hollowood, Timothy J.; Shore, Graham M. [Department of Physics, Swansea University,Swansea, SA2 8PP (United Kingdom)

    2016-03-18

    The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of “time machines”, i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.

  6. Causal Relationships Between Financial and Economic Development in Gulf Countries = Körfez Ülkelerinde Finansal ve Ekonomik Gelişme Arasındaki Nedensel İlişki

    Directory of Open Access Journals (Sweden)

    Hassan AL-AALİ

    2000-01-01

    Full Text Available This paper examines the causal relationships between financial and economic aggregates in three Gulf countries, Bahrain, Kuwait, and Saudi Arabia, over the 64?quarterly period from 1973 to 1988.Patrick's causality patterns at different stages of economic development were also investigated by dividing the entire analysis period into the sub-periods of 1973?81, and 1982?88. Financial variables used were M1, M2 and the total bank credits. Exports in all the three countries plus government expenditures in Kuwait were employed as proxies to GDP.Sims' causality model which is based on Granger's definition was utilized and the following general patterns were detected: For the entire analysis period causality ran from financial to economic variables in Kuwait, but from economic to financial variables in Bahrain. While no generalization was possible for Saudi Arabia for the first sub-period (l973?81, a supply-leading phenomenon was dominant in Bahrain and Saudi Arabia. In Kuwait the results were mixed. In the second sub-period (1982?88, the dominant relationship was demand following in all the three countries. These results were seen in conformity with the economic trends in these countries over the study period.

  7. A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

    Science.gov (United States)

    Butera, Nicole M; Lanza, Stephanie T; Coffman, Donna L

    2014-06-01

    Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

  8. Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

    Science.gov (United States)

    Liu, Shao-Hsien; Ulbricht, Christine M; Chrysanthopoulou, Stavroula A; Lapane, Kate L

    2016-07-20

    Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.

  9. Drawing causal inferences using propensity scores: a practical guide for community psychologists.

    Science.gov (United States)

    Lanza, Stephanie T; Moore, Julia E; Butera, Nicole M

    2013-12-01

    Confounding present in observational data impede community psychologists' ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods-weighting, matching, and subclassification-is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.

  10. Illness causal beliefs in Turkish immigrants

    Directory of Open Access Journals (Sweden)

    Klimidis Steven

    2007-07-01

    Full Text Available Abstract Background People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Methods Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Results Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Conclusion Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes

  11. Illness causal beliefs in Turkish immigrants.

    Science.gov (United States)

    Minas, Harry; Klimidis, Steven; Tuncer, Can

    2007-07-24

    People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different

  12. Entanglement entropy in causal set theory

    Science.gov (United States)

    Sorkin, Rafael D.; Yazdi, Yasaman K.

    2018-04-01

    Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to render entanglement entropy finite. Formulating a notion of entanglement entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which its definition typically relies is not available. Instead, we appeal to the more global expression given in Sorkin (2012 (arXiv:1205.2953)) which, for a Gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region (its ‘Wightman function’ W(x, x') ). Carrying this formula over to the causal set, one obtains an entropy which is both finite and of a Lorentz invariant nature. We evaluate this global entropy-expression numerically for certain regions (primarily order-intervals or ‘causal diamonds’) within causal sets of 1  +  1 dimensions. For the causal-set counterpart of the entanglement entropy, we obtain, in the first instance, a result that follows a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one obtains an area law if one truncates the commutator function (‘Pauli–Jordan operator’) and the Wightman function by projecting out the eigenmodes of the Pauli–Jordan operator whose eigenvalues are too close to zero according to a geometrical criterion which we describe more fully below. In connection with these results and the questions they raise, we also study the ‘entropy of coarse-graining’ generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same, ‘universal’ behaviour in both cases.

  13. Inferring the conservative causal core of gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Emmert-Streib Frank

    2010-09-01

    Full Text Available Abstract Background Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. Results In this paper, we introduce a novel gene regulatory network inference (GRNI algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. Conclusions For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  14. Inferring the conservative causal core of gene regulatory networks.

    Science.gov (United States)

    Altay, Gökmen; Emmert-Streib, Frank

    2010-09-28

    Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically. In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from E. coli that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

  15. Implications about the causality principle in the business income tax

    Directory of Open Access Journals (Sweden)

    Luis Durán Rojo

    2009-06-01

    Full Text Available The following article presents the implications about the practice of the causality principle for the determination of the income set with intention to apply the business income tax.We start considering the fact that this tax can be imposed to acquire goods known as a deductible expense of the practice, but not from those that are going to be part of the compatible cost to expropriate. Then, we make an extensive analysis about the way the Peruvian income tax law has configured the approaches of this principle and the understanding emerged from important jurisprudence cases from the members that solve problems, specially the Tax Court, when adopting a fast principle of expenses without causes.At the same time, this article describes the achievements of the rational and normality cost principles, so important for the evaluation of the performance of the principle of causality.Finally, we present some ideas about the accreditation of the cost facing and its relation to the causality principle.

  16. In defense of causal-formative indicators: A minority report.

    Science.gov (United States)

    Bollen, Kenneth A; Diamantopoulos, Adamantios

    2017-09-01

    Causal-formative indicators directly affect their corresponding latent variable. They run counter to the predominant view that indicators depend on latent variables and are thus often controversial. If present, such indicators have serious implications for factor analysis, reliability theory, item response theory, structural equation models, and most measurement approaches that are based on reflective or effect indicators. Psychological Methods has published a number of influential articles on causal and formative indicators as well as launching the first major backlash against them. This article examines 7 common criticisms of these indicators distilled from the literature: (a) A construct measured with "formative" indicators does not exist independently of its indicators; (b) Such indicators are causes rather than measures; (c) They imply multiple dimensions to a construct and this is a liability; (d) They are assumed to be error-free, which is unrealistic; (e) They are inherently subject to interpretational confounding; (f) They fail proportionality constraints; and (g) Their coefficients should be set in advance and not estimated. We summarize each of these criticisms and point out the flaws in the logic and evidence marshaled in their support. The most common problems are not distinguishing between what we call causal-formative and composite-formative indicators, tautological fallacies, and highlighting issues that are common to all indicators, but presenting them as special problems of causal-formative indicators. We conclude that measurement theory needs (a) to incorporate these types of indicators, and (b) to better understand their similarities to and differences from traditional indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  17. Causal mediation analysis with multiple mediators.

    Science.gov (United States)

    Daniel, R M; De Stavola, B L; Cousens, S N; Vansteelandt, S

    2015-03-01

    In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from "single mediator theory" to "multiple mediator practice," highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed. © 2014 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

  18. Inverse odds ratio-weighted estimation for causal mediation analysis.

    Science.gov (United States)

    Tchetgen Tchetgen, Eric J

    2013-11-20

    An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio-weighted approach to estimate so-called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright © 2013 John Wiley & Sons, Ltd.

  19. On the notion of causality in medicine: addressing Austin Bradford Hill and John L. Mackie

    Directory of Open Access Journals (Sweden)

    Luís Fernando S. C. de Araújo

    2014-03-01

    Full Text Available Almost 50 years ago appeared the seminal article by Austin Bradford Hill where he presented parameters for inferring causes from statistical associations, which became known as Hill’s causal criteria. This was a milestone for the renewal of the idea of cause in medicine. Our article revisits his contribution in light of the ideas from the Australian philosopher John L. Mackie, whose important works on causality reached an audience distinct from Hill’s. We suggest that both the British epidemiologist and the Australian philosopher share the purpose of articulating probabilistic determinism and multi-causality, the first with a predominantly probabilistic model and the second with an analytical approach. This article explores the possible consequences of addressing these authors jointly in regard to causal inferences in medicine, especially in respect to mental disorders.

  20. The Relevance of Causal Social Construction

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    Marques Teresa

    2017-02-01

    Full Text Available Social constructionist claims are surprising and interesting when they entail that presumably natural kinds are in fact socially constructed. The claims are interesting because of their theoretical and political importance. Authors like Díaz-León argue that constitutive social construction is more relevant for achieving social justice than causal social construction. This paper challenges this claim. Assuming there are socially salient groups that are discriminated against, the paper presents a dilemma: if there were no constitutively constructed social kinds, the causes of the discrimination of existing social groups would have to be addressed, and understanding causal social construction would be relevant to achieve social justice. On the other hand, not all possible constitutively socially constructed kinds are actual social kinds. If an existing social group is constitutively constructed as a social kind K, the fact that it actually exists as a K has social causes. Again, causal social construction is relevant. The paper argues that (i for any actual social kind X, if X is constitutively socially constructed as K, then it is also causally socially constructed; and (ii causal social construction is at least as relevant as constitutive social construction for concerns of social justice. For illustration, I draw upon two phenomena that are presumed to contribute towards the discrimination of women: (i the poor performance effects of stereotype threat, and (ii the silencing effects of gendered language use.