Causal inference in econometrics
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.
Causal inference based on counterfactuals
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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.
Optimal causal inference: estimating stored information and approximating causal architecture.
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.
Principal stratification in causal inference.
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.
Quasi-Experimental Designs for Causal Inference
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…
Introductive remarks on causal inference
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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.
Inferring causality from noisy time series data
DEFF Research Database (Denmark)
Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian
2016-01-01
Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...
Causal inference, probability theory, and graphical insights.
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.
Elements of Causal Inference: Foundations and Learning Algorithms
DEFF Research Database (Denmark)
Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...
Causal Inference in the Perception of Verticality.
de Winkel, Ksander N; Katliar, Mikhail; Diers, Daniel; Bülthoff, Heinrich H
2018-04-03
The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body's inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.
Statistical causal inferences and their applications in public health research
Wu, Pan; Chen, Ding-Geng
2016-01-01
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
Analogy in causal inference: rethinking Austin Bradford Hill's neglected consideration.
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.
God Does Not Play Dice: Causal Determinism and Preschoolers' Causal Inferences
Schulz, Laura E.; Sommerville, Jessica
2006-01-01
Three studies investigated children's belief in causal determinism. If children are determinists, they should infer unobserved causes whenever observed causes appear to act stochastically. In Experiment 1, 4-year-olds saw a stochastic generative cause and inferred the existence of an unobserved inhibitory cause. Children traded off inferences…
Controlling Selection Bias in Causal Inference
2012-02-01
and cervix . Journal of the National Cancer Institute 11 1269–1275. Didelez, V., Kreiner, S. and Keiding, N. (2010). Graphical models for inference...Endometrial Cancer (Y ) was overestimated in the data studied. One of the symptoms of the use of Oe- strogen is vaginal bleeding (W ) (Fig. 1(c)), and the...whether similar bounds can be de - rived in the presence of selection bias. We will show that selection bias can be removed entirely through the use of
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan
2016-09-01
Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.
Explanation in causal inference methods for mediation and interaction
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.
Meaningful mediation analysis : Plausible causal inference and informative communication
Pieters, Rik
2017-01-01
Statistical mediation analysis has become the technique of choice in consumer research to make causal inferences about the influence of a treatment on an outcome via one or more mediators. This tutorial aims to strengthen two weak links that impede statistical mediation analysis from reaching its
A Causal Inference Analysis of the Effect of Wildland Fire ...
Wildfire smoke is a major contributor to ambient air pollution levels. In this talk, we develop a spatio-temporal model to estimate the contribution of fire smoke to overall air pollution in different regions of the country. We combine numerical model output with observational data within a causal inference framework. Our methods account for aggregation and potential bias of the numerical model simulation, and address uncertainty in the causal estimates. We apply the proposed method to estimation of ozone and fine particulate matter from wildland fires and the impact on health burden assessment. We develop a causal inference framework to assess contributions of fire to ambient PM in the presence of spatial interference.
A Bayesian nonparametric approach to causal inference on quantiles.
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.
Simultaneous inference of haplotypes and alleles at a causal gene
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Fabrice eLarribe
2015-10-01
Full Text Available We present a new methodology which jointly infers haplotypes and the causal alleles at a gene influencing a given trait. Often in human genetic studies, the available data consists of genotypes (series of genetic markers along the chromosomes and a phenotype. However, for many genetic analyses, one needs haplotypes instead of genotypes. Our methodology is not only able to estimate haplotypes conditionally on the disease status, but is also able to infer the alleles at the unknown disease locus. Some applications of our methodology are in genetic mapping and in genetic counselling.
Research designs and making causal inferences from health care studies.
Flannelly, Kevin J; Jankowski, Katherine R B
2014-01-01
This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
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Daniel Ho
2011-08-01
Full Text Available MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007 for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
Form and function: Optional complementizers reduce causal inferences
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Hannah Rohde
2017-05-01
Full Text Available Many factors are known to influence the inference of the discourse coherence relationship between two sentences. Here, we examine the relationship between two conjoined embedded clauses in sentences like 'The professor noted that the student teacher did not look confident and (that the students were poorly behaved'. In two studies, we find that the presence of 'that 'before the second embedded clause in such sentences reduces the possibility of a forward causal relationship between the clauses, i.e., the inference that the student teacher’s confidence was what affected student behavior. Three further studies tested the possibility of a backward causal relationship between clauses in the same structure, and found that the complementizer’s presence aids that relationship, especially in a forced-choice paradigm. The empirical finding that a complementizer, a linguistic element associated primarily with structure rather than event-level semantics, can affect discourse coherence is novel and illustrates an interdependence between syntactic parsing and discourse parsing.
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, George; Hemani, Gibran
2014-01-01
Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research. PMID:25064373
Fourtune, Lisa; Prunier, Jérôme G; Paz-Vinas, Ivan; Loot, Géraldine; Veyssière, Charlotte; Blanchet, Simon
2018-04-01
Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic data sets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods-that is, maximum-likelihood path analysis and the directional separation test-by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fits of the tested models. We used an empirical data set combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature, and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.
Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz
2016-03-01
In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal
Non-Bayesian Inference: Causal Structure Trumps Correlation
Bes, Benedicte; Sloman, Steven; Lucas, Christopher G.; Raufaste, Eric
2012-01-01
The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more…
Causal inference in neuronal time-series using adaptive decomposition.
Rodrigues, João; Andrade, Alexandre
2015-04-30
The assessment of directed functional connectivity from neuronal data is increasingly common in neuroscience by applying measures based in the Granger causality (GC) framework. Although initially these consisted in simple analyses based on directionality strengths, current methods aim to discriminate causal effects both in time and frequency domain. We study the effect of adaptive data analysis on the GC framework by combining empirical mode decomposition (EMD) and causal analysis of neuronal signals. EMD decomposes data into simple amplitude and phase modulated oscillatory modes, the intrinsic mode functions (IMFs), from which it is possible to compute their instantaneous frequencies (IFs). Hence, we propose a method where causality is estimated between IMFs with comparable IFs, in a static or time-varying procedure, and then attributed to the frequencies corresponding to the IF of the driving IMF for improved frequency localization. We apply a thorough simulation framework involving all possible combinations of EMD algorithms with causality metrics and realistically simulated datasets. Results show that synchrosqueezing wavelet transform and noise-assisted multivariate EMD, paired with generalized partial directed coherence or with Geweke's GC, provide the highest sensitivity and specificity results. Compared to standard causal analysis, the output of selected representative instances of this methodology result in the fulfillment of performance criteria in a well-known benchmark with real animal epicranial recordings and improved frequency resolution for simulated neural data. This study presents empirical evidence that adaptive data analysis is a fruitful addition to the existing causal framework. Copyright © 2015 Elsevier B.V. All rights reserved.
Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.
Schuler, Megan S; Rose, Sherri
2017-01-01
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood estimation (TMLE) is a well-established alternative method with desirable statistical properties. TMLE is a doubly robust maximum-likelihood-based approach that includes a secondary "targeting" step that optimizes the bias-variance tradeoff for the target parameter. Under standard causal assumptions, estimates can be interpreted as causal effects. Because TMLE has not been as widely implemented in epidemiologic research, we aim to provide an accessible presentation of TMLE for applied researchers. We give step-by-step instructions for using TMLE to estimate the average treatment effect in the context of an observational study. We discuss conceptual similarities and differences between TMLE and 2 common estimation approaches (G-computation and inverse probability weighting) and present findings on their relative performance using simulated data. Our simulation study compares methods under parametric regression misspecification; our results highlight TMLE's property of double robustness. Additionally, we discuss best practices for TMLE implementation, particularly the use of ensembled machine learning algorithms. Our simulation study demonstrates all methods using super learning, highlighting that incorporation of machine learning may outperform parametric regression in observational data settings. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Children's Causal Inferences from Conflicting Testimony and Observations
Bridgers, Sophie; Buchsbaum, Daphna; Seiver, Elizabeth; Griffiths, Thomas L.; Gopnik, Alison
2016-01-01
Preschoolers use both direct observation of statistical data and informant testimony to learn causal relationships. Can children integrate information from these sources, especially when source reliability is uncertain? We investigate how children handle a conflict between what they hear and what they see. In Experiment 1, 4-year-olds were…
Rational Variability in Children's Causal Inferences: The Sampling Hypothesis
Denison, Stephanie; Bonawitz, Elizabeth; Gopnik, Alison; Griffiths, Thomas L.
2013-01-01
We present a proposal--"The Sampling Hypothesis"--suggesting that the variability in young children's responses may be part of a rational strategy for inductive inference. In particular, we argue that young learners may be randomly sampling from the set of possible hypotheses that explain the observed data, producing different hypotheses with…
The causal inference of cortical neural networks during music improvisations.
Directory of Open Access Journals (Sweden)
Xiaogeng Wan
Full Text Available We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME, to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.
The causal inference of cortical neural networks during music improvisations.
Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft
2014-01-01
We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.
The Causal Inference of Cortical Neural Networks during Music Improvisations
Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft
2014-01-01
We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions. PMID:25489852
Causal inference with missing exposure information: Methods and applications to an obstetric study.
Zhang, Zhiwei; Liu, Wei; Zhang, Bo; Tang, Li; Zhang, Jun
2016-10-01
Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and for causal inference with complete data (i.e. no unmeasured confounding and another positivity assumption). These methods involve three models: one for treatment assignment, one for the dependence of outcome on treatment and covariates, and one for the missing data mechanism. In general, consistent estimation of causal quantities requires correct specification of at least two of the three models, although there may be some flexibility as to which two models need to be correct. Such flexibility is afforded by doubly robust estimators adapted from the missing covariates literature and the literature on causal inference with complete data, and by a newly developed triply robust estimator that is consistent if any two of the three models are correct. The methods are applied to the Consortium on Safe Labor data and compared in a simulation study mimicking the Consortium on Safe Labor. © The Author(s) 2013.
Snowden, Jonathan M; Rose, Sherri; Mortimer, Kathleen M
2011-04-01
The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular.
Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions
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Felix Agakov
2011-12-01
Full Text Available We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of genotypes on outcomes. The method exploits the use of genotypes as “instrumental variables” to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. The framework builds on sparse linear methods developed in statistics and machine learning and modified here for inferring structures of richer networks with latent variables. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.
2018-01-01
Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181
Bayesian nonparametric generative models for causal inference with missing at random covariates.
Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J
2018-03-26
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.
Causal inference and longitudinal data: a case study of religion and mental health.
VanderWeele, Tyler J; Jackson, John W; Li, Shanshan
2016-11-01
We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.
Burgess, Stephen; Bowden, Jack; Fall, Tove; Ingelsson, Erik; Thompson, Simon G
2017-01-01
Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.
Proceeding From Observed Correlation to Causal Inference: The Use of Natural Experiments.
Rutter, Michael
2007-12-01
This article notes five reasons why a correlation between a risk (or protective) factor and some specified outcome might not reflect environmental causation. In keeping with numerous other writers, it is noted that a causal effect is usually composed of a constellation of components acting in concert. The study of causation, therefore, will necessarily be informative on only one or more subsets of such components. There is no such thing as a single basic necessary and sufficient cause. Attention is drawn to the need (albeit unobservable) to consider the counterfactual (i.e., what would have happened if the individual had not had the supposed risk experience). Fifteen possible types of natural experiments that may be used to test causal inferences with respect to naturally occurring prior causes (rather than planned interventions) are described. These comprise five types of genetically sensitive designs intended to control for possible genetic mediation (as well as dealing with other issues), six uses of twin or adoptee strategies to deal with other issues such as selection bias or the contrasts between different environmental risks, two designs to deal with selection bias, regression discontinuity designs to take into account unmeasured confounders, and the study of contextual effects. It is concluded that, taken in conjunction, natural experiments can be very helpful in both strengthening and weakening causal inferences. © 2007 Association for Psychological Science.
Liu, Yuqiong; Du, Qingyun; Wang, Qi; Yu, Huanyun; Liu, Jianfeng; Tian, Yu; Chang, Chunying; Lei, Jing
2017-07-01
The causation between bioavailability of heavy metals and environmental factors are generally obtained from field experiments at local scales at present, and lack sufficient evidence from large scales. However, inferring causation between bioavailability of heavy metals and environmental factors across large-scale regions is challenging. Because the conventional correlation-based approaches used for causation assessments across large-scale regions, at the expense of actual causation, can result in spurious insights. In this study, a general approach framework, Intervention calculus when the directed acyclic graph (DAG) is absent (IDA) combined with the backdoor criterion (BC), was introduced to identify causation between the bioavailability of heavy metals and the potential environmental factors across large-scale regions. We take the Pearl River Delta (PRD) in China as a case study. The causal structures and effects were identified based on the concentrations of heavy metals (Zn, As, Cu, Hg, Pb, Cr, Ni and Cd) in soil (0-20 cm depth) and vegetable (lettuce) and 40 environmental factors (soil properties, extractable heavy metals and weathering indices) in 94 samples across the PRD. Results show that the bioavailability of heavy metals (Cd, Zn, Cr, Ni and As) was causally influenced by soil properties and soil weathering factors, whereas no causal factor impacted the bioavailability of Cu, Hg and Pb. No latent factor was found between the bioavailability of heavy metals and environmental factors. The causation between the bioavailability of heavy metals and environmental factors at field experiments is consistent with that on a large scale. The IDA combined with the BC provides a powerful tool to identify causation between the bioavailability of heavy metals and environmental factors across large-scale regions. Causal inference in a large system with the dynamic changes has great implications for system-based risk management. Copyright © 2017 Elsevier Ltd. All
CauseMap: fast inference of causality from complex time series
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M. Cyrus Maher
2015-03-01
Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement
Snowden, Jonathan M.; Rose, Sherri; Mortimer, Kathleen M.
2011-01-01
The growing body of work in the epidemiology literature focused on G-computation includes theoretical explanations of the method but very few simulations or examples of application. The small number of G-computation analyses in the epidemiology literature relative to other causal inference approaches may be partially due to a lack of didactic explanations of the method targeted toward an epidemiology audience. The authors provide a step-by-step demonstration of G-computation that is intended to familiarize the reader with this procedure. The authors simulate a data set and then demonstrate both G-computation and traditional regression to draw connections and illustrate contrasts between their implementation and interpretation relative to the truth of the simulation protocol. A marginal structural model is used for effect estimation in the G-computation example. The authors conclude by answering a series of questions to emphasize the key characteristics of causal inference techniques and the G-computation procedure in particular. PMID:21415029
A new method to infer causal phenotype networks using QTL and phenotypic information.
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Huange Wang
Full Text Available In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.
Mendelian Randomization versus Path Models: Making Causal Inferences in Genetic Epidemiology.
Ziegler, Andreas; Mwambi, Henry; König, Inke R
2015-01-01
The term Mendelian randomization is popular in the current literature. The first aim of this work is to describe the idea of Mendelian randomization studies and the assumptions required for drawing valid conclusions. The second aim is to contrast Mendelian randomization and path modeling when different 'omics' levels are considered jointly. We define Mendelian randomization as introduced by Katan in 1986, and review its crucial assumptions. We introduce path models as the relevant additional component to the current use of Mendelian randomization studies in 'omics'. Real data examples for the association between lipid levels and coronary artery disease illustrate the use of path models. Numerous assumptions underlie Mendelian randomization, and they are difficult to be fulfilled in applications. Path models are suitable for investigating causality, and they should not be mixed up with the term Mendelian randomization. In many applications, path modeling would be the appropriate analysis in addition to a simple Mendelian randomization analysis. Mendelian randomization and path models use different concepts for causal inference. Path modeling but not simple Mendelian randomization analysis is well suited to study causality with different levels of 'omics' data. 2015 S. Karger AG, Basel.
Koenen, K C; Sumner, J A; Gilsanz, P; Glymour, M M; Ratanatharathorn, A; Rimm, E B; Roberts, A L; Winning, A; Kubzansky, L D
2017-01-01
Post-traumatic stress disorder (PTSD) has been declared 'a life sentence' based on evidence that the disorder leads to a host of physical health problems. Some of the strongest empirical research - in terms of methodology and findings - has shown that PTSD predicts higher risk of cardiometabolic diseases, specifically cardiovascular disease (CVD) and type 2 diabetes (T2D). Despite mounting evidence, PTSD is not currently acknowledged as a risk factor by cardiovascular or endocrinological medicine. This view is unlikely to change absent compelling evidence that PTSD causally contributes to cardiometabolic disease. This review suggests that with developments in methods for epidemiological research and the rapidly expanding knowledge of the behavioral and biological effects of PTSD the field is poised to provide more definitive answers to questions of causality. First, we discuss methods to improve causal inference using the observational data most often used in studies of PTSD and health, with particular reference to issues of temporality and confounding. Second, we consider recent work linking PTSD with specific behaviors and biological processes, and evaluate whether these may plausibly serve as mechanisms by which PTSD leads to cardiometabolic disease. Third, we evaluate how looking more comprehensively into the PTSD phenotype provides insight into whether specific aspects of PTSD phenomenology are particularly relevant to cardiometabolic disease. Finally, we discuss new areas of research that are feasible and could enhance understanding of the PTSD-cardiometabolic relationship, such as testing whether treatment of PTSD can halt or even reverse the cardiometabolic risk factors causally related to CVD and T2D.
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.
Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach
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Richard A. Ashley
2014-03-01
Full Text Available Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an “in-sample” period – purportedly utilized only for model specification/estimation – and a “post-sample” period, purportedly utilized (only at the end of the analysis for model validation/testing purposes. This partitioning is usually infeasible, however, with samples of modest length – e.g., T ≤ 150 – as is common in both quarterly data sets and/or in monthly data sets where institutional arrangements vary over time, simply because there is in such cases insufficient data available to credibly accomplish both purposes separately. A cross-sample validation (CSV testing procedure is proposed below which both eliminates the aforementioned a priori partitioning and which also substantially ameliorates this power versus credibility predicament – preserving most of the power of in-sample testing (by utilizing all of the sample data in the test, while also retaining most of the credibility of post-sample testing (by always basing model forecasts on data not utilized in estimating that particular model’s coefficients. Simulations show that the price paid, in terms of power relative to the in-sample Granger-causality F test, is manageable. An illustrative application is given, to a re-analysis of the Engel andWest [1] study of the causal relationship between macroeconomic fundamentals and the exchange rate; several of their conclusions are changed by our analysis.
Challenges to inferring causality from viral information dispersion in dynamic social networks
Ternovski, John
2014-06-01
Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.
Causal inference for Mann-Whitney-Wilcoxon rank sum and other nonparametric statistics.
Wu, P; Han, Y; Chen, T; Tu, X M
2014-04-15
The nonparametric Mann-Whitney-Wilcoxon (MWW) rank sum test is widely used to test treatment effect by comparing the outcome distributions between two groups, especially when there are outliers in the data. However, such statistics generally yield invalid conclusions when applied to nonrandomized studies, particularly those in epidemiologic research. Although one may control for selection bias by using available approaches of covariates adjustment such as matching, regression analysis, propensity score matching, and marginal structural models, such analyses yield results that are not only subjective based on how the outliers are handled but also often difficult to interpret. A popular alternative is a conditional permutation test based on randomization inference [Rosenbaum PR. Covariance adjustment in randomized experiments and observational studies. Statistical Science 2002; 17(3):286-327]. Because it requires strong and implausible assumptions that may not be met in most applications, this approach has limited applications in practice. In this paper, we address this gap in the literature by extending MWW and other nonparametric statistics to provide causal inference for nonrandomized study data by integrating the potential outcome paradigm with the functional response models (FRM). FRM is uniquely positioned to model dynamic relationships between subjects, rather than attributes of a single subject as in most regression models, such as the MWW test within our context. The proposed approach is illustrated with data from both real and simulated studies. Copyright © 2013 John Wiley & Sons, Ltd.
Auditory time-interval perception as causal inference on sound sources
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Ken-ichi eSawai
2012-11-01
Full Text Available Perception of a temporal pattern in a sub-second time scale is fundamental to conversation, music perception, and other kinds of sound communication. However, its mechanism is not fully understood. A simple example is hearing three successive sounds with short time intervals. The following misperception of the latter interval is known: underestimation of the latter interval when the former is a little shorter or much longer than the latter, and overestimation of the latter when the former is a little longer or much shorter than the latter. Although this misperception of auditory time intervals for simple stimuli might be a cue to understanding the mechanism of time-interval perception, there exist no model that comprehensively explains it. Considering a previous experiment demonstrating that illusory perception does not occur for stimulus sounds with different frequencies, it might be plausible to think that the underlying mechanism of time-interval perception involves a causal inference on sound sources: herein, different frequencies provide cues for different causes. We construct a Bayesian observer model of this time-interval perception. We introduce a probabilistic variable representing the causality of sounds in the model. As prior knowledge, the observer assumes that a single sound source produces periodic and short time intervals, which is consistent with several previous works. We conducted numerical simulations and confirmed that our model can reproduce the misperception of auditory time intervals. A similar phenomenon has also been reported in visual and tactile modalities, though the time ranges for these are wider. This suggests the existence of a common mechanism for temporal pattern perception over modalities. This is because these different properties can be interpreted as a difference in time resolutions, given that the time resolutions for vision and tactile are lower than those for audition.
Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference.
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.
An alternative empirical likelihood method in missing response problems and causal inference.
Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao
2016-11-30
Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Mendelian randomization: use of genetics to enable causal inference in observational studies
Verduijn, Marion; Siegerink, Bob; Jager, Kitty J.; Zoccali, Carmine; Dekker, Friedo W.
2010-01-01
The aim of aetiologic studies in epidemiology is to investigate whether factors are causally related to diseases and therefore become a potential target for therapeutic interventions. Mendelian randomization enables estimation of causal relationships in observational studies using genetic variants
Gross, Kevin; Rosenheim, Jay A
2011-10-01
Secondary pest outbreaks occur when the use of a pesticide to reduce densities of an unwanted target pest species triggers subsequent outbreaks of other pest species. Although secondary pest outbreaks are thought to be familiar in agriculture, their rigorous documentation is made difficult by the challenges of performing randomized experiments at suitable scales. Here, we quantify the frequency and monetary cost of secondary pest outbreaks elicited by early-season applications of broad-spectrum insecticides to control the plant bug Lygus spp. (primarily L. hesperus) in cotton grown in the San Joaquin Valley, California, USA. We do so by analyzing pest-control management practices for 969 cotton fields spanning nine years and 11 private ranches. Our analysis uses statistical methods to draw formal causal inferences from nonexperimental data that have become popular in public health and economics, but that are not yet widely known in ecology or agriculture. We find that, in fields that received an early-season broad-spectrum insecticide treatment for Lygus, 20.2% +/- 4.4% (mean +/- SE) of late-season pesticide costs were attributable to secondary pest outbreaks elicited by the early-season insecticide application for Lygus. In 2010 U.S. dollars, this equates to an additional $6.00 +/- $1.30 (mean +/- SE) per acre in management costs. To the extent that secondary pest outbreaks may be driven by eliminating pests' natural enemies, these figures place a lower bound on the monetary value of ecosystem services provided by native communities of arthropod predators and parasitoids in this agricultural system.
Case Studies Nested in Fuzzy-Set QCA on Sufficiency: Formalizing Case Selection and Causal Inference
Schneider, Carsten Q.; Rohlfing, Ingo
2016-01-01
Qualitative Comparative Analysis (QCA) is a method for cross-case analyses that works best when complemented with follow-up case studies focusing on the causal quality of the solution and its constitutive terms, the underlying causal mechanisms, and potentially omitted conditions. The anchorage of QCA in set theory demands criteria for follow-up…
The Effects of Simple Necessity and Sufficiency Relationships on Children's Causal Inferences
Siegler, Robert S.
1976-01-01
Attempted to determine (1) whether developmental differences existed in children's comprehension of simple necessity and simple sufficiency relationships, and (2) the source of developmental differences in children's causal reasoning. (SB)
A New Method to Infer Causal Phenotype Networks Using QTL and Phenotypic Information
Wang, H.; Eeuwijk, van F.
2014-01-01
In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at
Sex differences in the inference and perception of causal relations within a video game
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Michael E. Young
2014-08-01
Full Text Available The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.
Antonella Del Rosso
2012-01-01
CERN was founded 58 years ago under the auspices of UNESCO. Since then, both organisations have grown to become world leaders in their respective fields. The links between the two have always existed but today they are even stronger, with new projects under way to develop a more efficient way of exchanging information and devise a common strategy on topics of mutual interest. CERN and UNESCO are a perfect example of natural partners: their common field is science and education is one of the pillars on which both are built. Historically, they share a common heritage. Both UNESCO and CERN were born of the desire to use scientific cooperation to rebuild peace and security in the aftermath of the Second World War. "Recently, building on our common roots and in close collaboration with UNESCO, we have been developing more structured links to ensure the continuity of the actions taken over the years," says Maurizio Bona, who is in charge of CERN relations with international orga...
A framework for Bayesian nonparametric inference for causal effects of mediation.
Kim, Chanmin; Daniels, Michael J; Marcus, Bess H; Roy, Jason A
2017-06-01
We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivity parameters. For Part 1, we specify a Dirichlet process mixture of multivariate normals as a prior on the joint distribution of the outcome, mediator, and covariates. This approach allows us to obtain a (simple) closed form of each marginal distribution. For Part 2, we consider two sets of assumptions: (a) the standard sequential ignorability (Imai et al., 2010) and (b) weakened set of the conditional independence type assumptions introduced in Daniels et al. (2012) and propose sensitivity analyses for both. We use this approach to assess mediation in a physical activity promotion trial. © 2016, The International Biometric Society.
Causal inference regarding infectious aetiology of chronic conditions: a systematic review.
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Sofia Orrskog
Full Text Available BACKGROUND: The global burden of disease has shifted from communicable diseases in children to chronic diseases in adults. This epidemiologic shift varies greatly by region, but in Europe, chronic conditions account for 86% of all deaths, 77% of the disease burden, and up to 80% of health care expenditures. A number of risk factors have been implicated in chronic diseases, such as exposure to infectious agents. A number of associations have been well established while others remain uncertain. METHODS AND FINDINGS: We assessed the body of evidence regarding the infectious aetiology of chronic diseases in the peer-reviewed literature over the last decade. Causality was assessed with three different criteria: First, the total number of associations documented in the literature between each infectious agent and chronic condition; second, the epidemiologic study design (quality of the study; third, evidence for the number of Hill's criteria and Koch's postulates that linked the pathogen with the chronic condition. We identified 3136 publications, of which 148 were included in the analysis. There were a total of 75 different infectious agents and 122 chronic conditions. The evidence was strong for five pathogens, based on study type, strength and number of associations; they accounted for 60% of the associations documented in the literature. They were human immunodeficiency virus, hepatitis C virus, Helicobacter pylori, hepatitis B virus, and Chlamydia pneumoniae and were collectively implicated in the aetiology of 37 different chronic conditions. Other pathogens examined were only associated with very few chronic conditions (≤ 3 and when applying the three different criteria of evidence the strength of the causality was weak. CONCLUSIONS: Prevention and treatment of these five pathogens lend themselves as effective public health intervention entry points. By concentrating research efforts on these promising areas, the human, economic, and societal
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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.
Targeted learning in data science causal inference for complex longitudinal studies
van der Laan, Mark J
2018-01-01
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generatio...
α-Decomposition for estimating parameters in common cause failure modeling based on causal inference
International Nuclear Information System (INIS)
Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi
2013-01-01
The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system
Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan
2015-01-01
This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129
Chatterji, Madhabi
2016-12-01
This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention's effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Gastón Saux
2015-12-01
Full Text Available RESUMEN: Se examinó la generación de inferencias causal-antecedente durante y luego de la lectura de textos expositivos con contenidos científicos poco familiares en 52 estudiantes de grado universitario (Edad M = 24.48, DS = 3.6, quienes leyeron 24 textos científicos breves. Se registraron dos medidas de activación durante la lectura (tiempos de lectura y decisión léxica y una medida post-lectura (respuestas a preguntas; además, se controló el grado de familiaridad de los materiales. Los resultados sugieren que las inferencias causal-antecedente son generadas al leer materiales científicos poco familiares, pero el grado de activación de la información requerida por la inferencia depende de la distancia de las partes del texto a ser integradas. ABSTRACT: The generation of causal inferences-background was examined during and after the reading of expository texts with little unfamiliar scientific content in 52 university level students (M Age = 24.48, SD = 3.6, who read 24 short scientific texts. There were two activation measures during the reading (reading times and lexical decision and a post-reading measure (answers to questions; in addition, the degree of familiarity of the material was controlled. The results suggest that causal inferences-background is generated when reading unfamiliar scientific material, but the degree of activation of the information required by inference depends on the distance of the parts of the text to be integrated.
DEFF Research Database (Denmark)
Møller, Jesper
2010-01-01
Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....
DEFF Research Database (Denmark)
Møller, Jesper
(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...
Adami, Hans-Olov; Berry, Sir Colin L.; Breckenridge, Charles B.; Smith, Lewis L.; Swenberg, James A.; Trichopoulos, Dimitrios; Weiss, Noel S.; Pastoor, Timothy P.
2011-01-01
Historically, toxicology has played a significant role in verifying conclusions drawn on the basis of epidemiological findings. Agents that were suggested to have a role in human diseases have been tested in animals to firmly establish a causative link. Bacterial pathogens are perhaps the oldest examples, and tobacco smoke and lung cancer and asbestos and mesothelioma provide two more recent examples. With the advent of toxicity testing guidelines and protocols, toxicology took on a role that was intended to anticipate or predict potential adverse effects in humans, and epidemiology, in many cases, served a role in verifying or negating these toxicological predictions. The coupled role of epidemiology and toxicology in discerning human health effects by environmental agents is obvious, but there is currently no systematic and transparent way to bring the data and analysis of the two disciplines together in a way that provides a unified view on an adverse causal relationship between an agent and a disease. In working to advance the interaction between the fields of toxicology and epidemiology, we propose here a five-step “Epid-Tox” process that would focus on: (1) collection of all relevant studies, (2) assessment of their quality, (3) evaluation of the weight of evidence, (4) assignment of a scalable conclusion, and (5) placement on a causal relationship grid. The causal relationship grid provides a clear view of how epidemiological and toxicological data intersect, permits straightforward conclusions with regard to a causal relationship between agent and effect, and can show how additional data can influence conclusions of causality. PMID:21561883
La Bastide-Van Gemert, Sacha; Seggers, Jorien; Haadsma, Maaike L; Heineman, Maas Jan; Middelburg, Karin J; Roseboom, Tessa J; Schendelaar, Pamela; Hadders-Algra, Mijna; Van den Heuvel, Edwin R
2014-03-01
What causal relationships underlie the associations between ovarian stimulation, the IVF procedure, parental-, fertility- and child characteristics, and blood pressure (BP) and anthropometrics of 4-year-old IVF children? Causal models compatible with the data suggest the presence of positive direct effects of controlled ovarian hyperstimulation as applied in IVF (COH-IVF) on systolic blood pressure (SBP) percentiles and subscapular skinfold thickness. Increasing evidence suggests that IVF is associated with higher blood pressure and altered body fat distribution in offspring, but underlying mechanisms describing the causal relationships between the variables are largely unknown. In this assessor-blinded follow-up study, 194 children were assessed. The attrition rate until the 4-year-old assessment was 10%. We measured blood pressure and anthropometrics of 4-year-old singletons born following COH-IVF (n = 63), or born following modified natural cycle IVF (MNC-IVF, n = 52) or born to subfertile couples who conceived naturally (Sub-NC, n = 79). Primary outcome measures were the SBP and diastolic blood pressure (DBP) percentiles. Anthropometrics included triceps and subscapular skinfold thickness. Causal inference search algorithms and structural equation modeling were applied. Explorative analyses suggested a direct effect of COH on SBP percentiles and on subscapular skinfold thickness. This hypothesis needs confirmation with additional, preferably larger, studies. Search algorithms were used as explorative tools to generate hypotheses on the causal mechanisms underlying fertility treatment, blood pressure, anthropometrics and other variables. More studies using larger groups are needed to draw firm conclusions. Our findings are in line with other studies describing adverse effects of IVF on cardiometabolic outcome, but this is the first study suggesting a causal mechanism underlying this association. Perhaps ovarian hyperstimulation negatively influences
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.
Chaix, Basile; Méline, Julie; Duncan, Scott; Merrien, Claire; Karusisi, Noëlla; Perchoux, Camille; Lewin, Antoine; Labadi, Karima; Kestens, Yan
2013-05-01
Recent studies have relied on GPS tracking to assess exposure to environmental characteristics over daily life schedules. Combining GPS and GIS allows for advances in environmental exposure assessment. However, biases related to selective daily mobility preclude assessment of environmental effects, to the extent that these studies may represent a step backward in terms of assessment of causal effects. A solution may be to integrate the Public health / Nutrition approach and the Transportation approach to GPS studies, so as to combine a GPS and accelerometer data collection with an electronic mobility survey. Correcting exposure measures and improving study designs with this approach may permit mitigating biases related to selective daily mobility. Copyright © 2013 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Hsiao-Yu Yang
2014-01-01
Full Text Available Herbal remedies containing aristolochic acid (AA have been designated to be a strong carcinogen. This review summarizes major epidemiologic evidence to argue for the causal association between AA exposure and urothelial carcinoma as well as nephropathy. The exposure scenarios include the following: Belgian women taking slimming pills containing single material Guang Fang Ji, consumptions of mixtures of Chinese herbal products in the general population and patients with chronic renal failure in Taiwan, occupational exposure in Chinese herbalists, and food contamination in farming villages in valleys of the Danube River. Such an association is corroborated by detecting specific DNA adducts in the tumor tissue removed from affected patients. Preventive actions of banning such use and education to the healthcare professionals and public are necessary for the safety of herbal remedies.
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.
La Bastide-van Gemert, Sacha; Seggers, Jorien; Haadsma, Maaike L.; Heineman, Maas Jan; Middelburg, Karin J.; Roseboom, Tessa J.; Schendelaar, Pamela; Hadders-Algra, Mijna; van den Heuvel, Edwin R.
2014-01-01
What causal relationships underlie the associations between ovarian stimulation, the IVF procedure, parental-, fertility- and child characteristics, and blood pressure (BP) and anthropometrics of 4-year-old IVF children? Causal models compatible with the data suggest the presence of positive direct
La Bastide-Van Gemert, Sacha; Seggers, Jorien; Haadsma, Maaike L.; Heineman, Maas Jan; Middelburg, Karin J.; Roseboom, Tessa J.; Schendelaar, Pamela; Hadders-Algra, Mijna; Van den Heuvel, Edwin R.
STUDY QUESTION: What causal relationships underlie the associations between ovarian stimulation, the IVF procedure, parental-, fertility- and child characteristics, and blood pressure (BP) and anthropometrics of 4-year-old IVF children? SUMMARY ANSWER: Causal models compatible with the data suggest
Causality and headache triggers
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
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...
Classical planning and causal implicatures
DEFF Research Database (Denmark)
Blackburn, Patrick Rowan; Benotti, Luciana
for understanding the structure of task-oriented dialogues. Such dialogues locate conversational acts in contexts containing both pending tasks and the acts which bring them about. The ability to infer causal implicatures lets us interleave decisions about "how to sequence actions" with decisions about "when......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...
A theory of causal learning in children: Causal maps and Bayes nets
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...
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.
Bailly, Sébastien; Leroy, Olivier; Azoulay, Elie; Montravers, Philippe; Constantin, Jean-Michel; Dupont, Hervé; Guillemot, Didier; Lortholary, Olivier; Mira, Jean-Paul; Perrigault, Pierre-François; Gangneux, Jean-Pierre; Timsit, Jean-François
2017-04-01
guidelines recommend first-line systemic antifungal therapy (SAT) with echinocandins in invasive candidiasis (IC), especially in critically ill patients. This study aimed at assessing the impact of echinocandins compared to azoles as initial SAT on the 28-day prognosis in adult ICU patients. From the prospective multicenter AmarCAND2 cohort (835 patients), we selected those with documented IC and treated with echinocandins (ECH) or azoles (AZO). The average causal effect of echinocandins on 28-day mortality was assessed using an inverse probability of treatment weight (IPTW) estimator. 397 patients were selected, treated with echinocandins (242 patients, 61%) or azoles (155 patients, 39%); septic shock: 179 patients (45%). The median SAPSII was higher in the ECH group (48 [35; 62] vs. 43 [31; 58], p = 0.01). Crude mortality was 34% (ECH group) vs. 25% (AZO group). After adjustment on baseline confounders, no significant association emerged between initial SAT with echinocandins and 28-day mortality (HR: 0.95; 95% CI: [0.60; 1.49]; p = 0.82). However, echinocandin tended to benefit patients with septic shock (HR: 0.46 [0.19; 1.07]; p = 0.07). Patients who received echinocandins were more severely ill. Echinocandin use was associated with a non-significant 7% decrease of 28-day mortality and a trend to a beneficial effect for patient with septic shock. Copyright © 2017 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Rasmussen, Lauge Baungaard
2006-01-01
The lecture note explains how to use the causal mapping method as well as the theoretical framework aoosciated to the method......The lecture note explains how to use the causal mapping method as well as the theoretical framework aoosciated to the method...
A theory of causal learning in children: causal maps and Bayes nets.
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.
Prospects for stronger calandria tubes
International Nuclear Information System (INIS)
Ells, C.E.; Coleman, C.E.; Hosbons, R.R.; Ibrahim, E.F.; Doubt, G.L.
1990-12-01
The CANDU calandria tubes, made of seam welded and annealed Zircaloy-2, have given exemplary service in-reactor. Although not designed as a system pressure containment, calandria tubes may remain intact even in the face of pressure tube rupture. One such incident at Pickering Unit 2 demonstrated the economic advantage of such an outcome, and a case can be made for increasing the probability that other calandria tubes would perform in a similar fashion. Various methods of obtaining stronger calandria tubes are available, and reviewed here. When the tubes are internally pressurized, the weld is the weak section of the tube. Increasing the oxygen concentration in the starting sheet, and thickening the weld, are promising routes to a stronger tube
Granger Causality and Unit Roots
DEFF Research Database (Denmark)
Rodríguez-Caballero, Carlos Vladimir; Ventosa-Santaulària, Daniel
2014-01-01
, 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...... stochastic nonstationarity, a property typically found in many macroeconomic variables....
A Theory of Diagnostic Inference: Judging Causality.
1983-08-01
newspaper. They were offered $5 an hour to participate in an experiment on judgment. Their median age was 24, their educational level was high (mean of...4.4 years of post high school education ), and there were 16 males and 16 females. Stimuli. The stimuli consisted of eight scenarios varying in length...Cambridge, MA: Harvard Univesity Press, 1957. Bruner, J. S., & Postman, L. On the perception of incongruity: A paradigm. Journal of Personality, 1949, 18
Are melanized feather barbs stronger?
Butler, Michael; Johnson, Amy S
2004-01-01
Melanin has been associated with increased resistance to abrasion, decreased wear and lowered barb breakage in feathers. But, this association was inferred without considering barb position along the rachis as a potentially confounding variable. We examined the cross-sectional area, breaking force, breaking stress, breaking strain and toughness of melanized and unmelanized barbs along the entire rachis of a primary feather from an osprey (Pandion haliaetus). Although breaking force was higher for melanized barbs, breaking stress (force divided by cross-sectional area) was greater for unmelanized barbs. But when position was considered, all mechanical differences between melanized and unmelanized barbs disappeared. Barb breaking stress, breaking strain and toughness decreased, and breaking stiffness increased, distally along the rachis. These proximal-distal material property changes are small and seem unlikely to affect flight performance of barbs. Our observations of barb bending, breaking and morphology, however, lead us to propose a design principle for barbs. We propose that, by being thicker-walled dorso-ventrally, the barb's flexural stiffness is increased during flight; but, by allowing for twisting when loaded with dangerously high forces, barbs firstly avoid failure by bending and secondly avoid complete failure by buckling rather than rupturing.
Disagreement and causal learning: others' hypotheses affect children's evaluations of evidence.
Young, Andrew G; Alibali, Martha W; Kalish, Charles W
2012-09-01
When children evaluate evidence and make causal inferences, they are sensitive to the social context in which data are generated. This study investigated whether children learn more from evidence generated by an agent who agrees with them or from one who disagrees with them. Children in two age groups (5- and 6-year-olds and 9- and 10-year-olds) observed the functioning of a machine that lit up and played music in the presence of certain objects. After endorsing one of two plausible causal hypotheses, children observed a puppet either agree or disagree with their own hypotheses. The puppet then generated a further piece of evidence that confirmed, disconfirmed, or was neutral with respect to the children's hypotheses. When they were later asked to make causal inferences about objects they did not directly observe, children in both age groups responded differentially to identical evidence depending on whether the agent agreed or disagreed, and they often drew stronger inferences in response to disagreement. In addition, older children were particularly sensitive to disagreement when the evidence was ambiguous. Our results suggest that children consider the relationship between their own and others' hypotheses when evaluating evidence that others generate. PsycINFO Database Record (c) 2012 APA, all rights reserved.
Thinking Fast and Slow about Causality: Response to Palinkas
Marsh, Jeanne C.
2014-01-01
Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…
Repair of Partly Misspecified Causal Diagrams.
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.
Finding the Cause: Verbal Framing Helps Children Extract Causal Evidence Embedded in a Complex Scene
Butler, Lucas P.; Markman, Ellen M.
2012-01-01
In making causal inferences, children must both identify a causal problem and selectively attend to meaningful evidence. Four experiments demonstrate that verbally framing an event ("Which animals make Lion laugh?") helps 4-year-olds extract evidence from a complex scene to make accurate causal inferences. Whereas framing was unnecessary when…
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.
Gang membership and substance use: guilt as a gendered causal pathway.
Coffman, Donna L; Melde, Chris; Esbensen, Finn-Aage
2015-03-01
We examine whether anticipated guilt for substance use is a gendered mechanism underlying the noted enhancement effect of gang membership on illegal drug use. We also demonstrate a method for making stronger causal inferences when assessing mediation in the presence of moderation and time-varying confounding. We estimate a series of inverse propensity weighted models to obtain unbiased estimates of mediation in the presence of confounding of the exposure (i.e., gang membership) and mediator (i.e., anticipated guilt) using three waves of data from a multi-site panel study of a law-related education program for youth ( N =1,113). The onset of gang membership significantly decreased anticipated substance use guilt among both male and female respondents. This reduction was significantly associated with increased frequency of substance use only for female respondents, however, suggesting that gender moderates the mechanism through which gang membership influences substance use. Criminologists are often concerned with identifying causal pathways for antisocial and/or delinquent behavior, but confounders of the exposure, mediator, and outcome often interfere with efforts to assess mediation. Many new approaches have been proposed for strengthening causal inference for mediation effects. After controlling for confounding using inverse propensity weighting, our results suggest that interventions aimed at reducing substance use by current and former female gang members should focus on the normative aspects of these behaviors.
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)
On the origin of Hill's causal criteria.
Morabia, A
1991-09-01
The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.
A quantum probability model of causal reasoning
Directory of Open Access Journals (Sweden)
Jennifer S Trueblood
2012-05-01
Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.
Causal binding of actions to their effects.
Buehner, Marc J; Humphreys, Gruffydd R
2009-10-01
According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.
Chemical reaction due to stronger Ramachandran interaction
Indian Academy of Sciences (India)
The origin of a chemical reaction between two reactant atoms is associated with the activation energy, on the assumption that, high-energy collisions between these atoms, are the ones that overcome the activation energy. Here, we show that a stronger attractive van der Waals (vdW) and electron-ion Coulomb interactions ...
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.
Temporal and Statistical Information in Causal Structure Learning
McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David
2015-01-01
Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…
Weighting-Based Sensitivity Analysis in Causal Mediation Studies
Hong, Guanglei; Qin, Xu; Yang, Fan
2018-01-01
Through a sensitivity analysis, the analyst attempts to determine whether a conclusion of causal inference could be easily reversed by a plausible violation of an identification assumption. Analytic conclusions that are harder to alter by such a violation are expected to add a higher value to scientific knowledge about causality. This article…
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...
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...... of discursive causalities towards more substantive claims of causality between EU policy and institutional initiatives and domestic change....
Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables
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.
Using Propensity Score Analysis for Making Causal Claims in Research Articles
Bai, Haiyan
2011-01-01
The central role of the propensity score analysis (PSA) in observational studies is for causal inference; as such, PSA is often used for making causal claims in research articles. However, there are still some issues for researchers to consider when making claims of causality using PSA results. This summary first briefly reviews PSA, followed by…
Kant on causal laws and powers.
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.
LHC Season 2: A stronger machine
Dominguez, Daniel
2015-01-01
1) New magnets / De nouveaux aimants 2) Stronger connections / Des jonctions électriques renforcées 3) Safer magnets / Des aimants plus sûrs 4) Higher energy beams / Des faisceaux d’énergie plus élevée 5) Narrower beams / Des faisceaux plus serrés 6) Smaller but closer proton packets / Des groupes de protons plus petits mais plus rapprochés 7) Higher voltage / Une tension plus haute 8) Superior cryogenics / Un système cryogénique amélioré 9) Radiation-resistant electronics / Une électronique qui résiste aux radiations 10) More secure vacuum / Un vide plus sûr
A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.
Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L
2016-03-01
Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015
Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.
1995-01-01
The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is
Causality in Classical Physics
Indian Academy of Sciences (India)
IAS Admin
Classical physics encompasses the study of phys- ical phenomena which range from local (a point) to nonlocal (a region) in space and/or time. We discuss the concept of spatial and temporal non- locality. However, one of the likely implications pertaining to nonlocality is non-causality. We study causality in the context of ...
Causality in Classical Electrodynamics
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…
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......This article focuses on causality in demand. A methodology where causality is imposed and tested within an empirical co-integrated demand model, not prespecified, is suggested. The methodology allows different causality of different products within the same demand system. The methodology is applied...... implication is that more explicit focus on causality in demand analyses provides improved information. The results suggest that frozen trout forms part of a large European whitefish market, where prices of fresh trout are formed on a relatively separate market. Redfish is a substitute on both markets...
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.
Gas Marbles: Much Stronger than Liquid Marbles
Timounay, Yousra; Pitois, Olivier; Rouyer, Florence
2017-06-01
Enwrapping liquid droplets with hydrophobic particles allows the manufacture of so-called "liquid marbles" [Aussillous and Quéré Nature (London) 411, 924 (2001); , 10.1038/35082026Mahadevan Nature (London)411, 895 (2001), 10.1038/35082164]. The recent intensive research devoted to liquid marbles is justified by their very unusual physical and chemical properties and by their potential for various applications, from microreactors to water storage, including water pollution sensors [Bormashenko Curr. Opin. Colloid Interface Sci. 16, 266 (2011), 10.1016/j.cocis.2010.12.002]. Here we demonstrate that this concept can be successfully applied for encapsulating and protecting small gas pockets within an air environment. Similarly to their liquid counterparts, those new soft-matter objects, that we call "gas marbles," can sustain external forces. We show that gas marbles are surprisingly tenfold stronger than liquid marbles and, more importantly, they can sustain both positive and negative pressure differences. This magnified strength is shown to originate from the strong cohesive nature of the shell. Those interesting properties could be exploited for imprisoning valuable or polluted gases or for designing new aerated materials.
Statistical inference a short course
Panik, Michael J
2012-01-01
A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal
2012-10-01
The following five articles have been retracted from Personality and Social Psychology Bulletin by the Society for Personality and Social Psychology, and the Editor and the publisher of the journal: Avramova, Y.R., Stapel, D.A. & Lerouge, D. (2010). The influence of mood on attribution. Personality and Social Psychology Bulletin, 36, 1360-1371. (Original DOI: 10.1177/0146167210381083) Noordewier, M.K., & Stapel, D.A. (2010). Affects of the unexpected: When inconsistency feels good (or bad). Personality and Social Psychology Bulletin, 36, 642-654. (Original DOI: 10.1177/0146167209357746 ) Van den Bos, A., & Stapel, D.A. (2009). Why people stereotype affects how they stereotype: The differential influence of comprehension goals and self-enhancement goals on stereotyping. Personality and Social Psychology Bulletin, 35(1), 101-113 (Original DOI: 10.1177/0146167208325773) Joly, J.F., Stapel, D.A., & Lindenberg, S.M. (2008). Silence and table manners: When environments activate norms. Personality and Social Psychology Bulletin, 34(8), 1047-1056 (Original DOI: 10.1177/0146167208318401) Stapel, D. A., & Spears, R. (1996). Event accessibility and context effects in causal inference: Judgment of a different order. Personality and Social Psychology Bulletin, 22, 979-992. (Original DOI: 10.1177/01461672962210001).
States agree on stronger physical protection regime
International Nuclear Information System (INIS)
2005-01-01
Full text: Delegates from 89 countries agreed on 8 July to fundamental changes that will substantially strengthen the Convention on the Physical Protection of Nuclear Material (CPPNM). IAEA Director General Mohamed ElBaradei welcomed the agreement in saying 'This new and stronger treaty is an important step towards greater nuclear security by combating, preventing, and ultimately punishing those who would engage in nuclear theft, sabotage or even terrorism. It demonstrates that there is indeed a global commitment to remedy weaknesses in our nuclear security regime.' The amended CPPNM makes it legally binding for States Parties to protect nuclear facilities and material in peaceful domestic use, storage as well as transport. It will also provide for expanded cooperation between and among States regarding rapid measures to locate and recover stolen or smuggled nuclear material, mitigate any radiological consequences of sabotage, and prevent and combat related offences. The original CPPNM applied only to nuclear material in international transport. Conference President Dr. Alec Baer said 'All 89 delegations demonstrated real unity of purpose. They put aside some very genuine national concerns in favour of the global interest and the result is a much improved convention that is better suited to addressing the nuclear security challenges we currently face.' The new rules will come into effect once they have been ratified by two-thirds of the 112 States Parties of the Convention, expected to take several years. 'But concrete actions are already taking place around the world. For more than 3 years, the IAEA has been implementing a systematic Nuclear Security plan, including physical protection activities designed to prevent, detect and respond to malicious acts,' said Anita Nillson, Director of the IAEA's Office of Nuclear Security. The Agency's Nuclear Security Fund, set up after the events of 9/11, has delivered $19.5 million in practical assistance to 121 countries
Causal Imprinting in Causal Structure Learning
Taylor, Eric G.; Ahn, Woo-kyoung
2012-01-01
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…
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.
Efficient design and inference in distributed Bayesian networks: an overview
de Oude, P.; Groen, F.C.A.; Pavlin, G.; Bezhanishvili, N.; Löbner, S.; Schwabe, K.; Spada, L.
2011-01-01
This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed
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)
A general, multivariate definition of causal effects in epidemiology.
Flanders, W Dana; Klein, Mitchel
2015-07-01
Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.
Beyond Markov: Accounting for independence violations in causal reasoning.
Rehder, Bob
2018-03-06
Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.
Rideout, D P
2001-01-01
The Causal Set approach to quantum gravity asserts that spacetime, at its smallest length scale, has a discrete structure. This discrete structure takes the form of a locally finite order relation, where the order, corresponding with the macroscopic notion of spacetime causality, is taken to be a fundamental aspect of nature. After an introduction to the Causal Set approach, this thesis considers a simple toy dynamics for causal sets. Numerical simulations of the model provide evidence for the existence of a continuum limit. While studying this toy dynamics, a picture arises of how the dynamics can be generalized in such a way that the theory could hope to produce more physically realistic causal sets. By thinking in terms of a stochastic growth process, and positing some fundamental principles, we are led almost uniquely to a family of dynamical laws (stochastic processes) parameterized by a countable sequence of coupling constants. This result is quite promising in that we now know how to speak of dynamics ...
Linear and nonlinear causality between renewable energy consumption and economic growth in the USA
Xu, Haiyun
2016-01-01
This study aims to investigate Granger causality between renewable energy consumption (REC) and economic growth (EG) for USA. To accomplish this objective and to add the stronger evidence to the controversial issue, the tests were done under a new framework that embeds wavelet analysis, a novel tool, in nonlinear causality test approaches developed recently. The classical linear causality test procedure was also involved for comparison. The empirical data sources from the US...
A quantum causal discovery algorithm
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.
Emoticons vs. Emojis on Twitter: A Causal Inference Approach
Pavalanathan, Umashanthi; Eisenstein, Jacob
2015-01-01
Online writing lacks the non-verbal cues present in face-to-face communication, which provide additional contextual information about the utterance, such as the speaker's intention or affective state. To fill this void, a number of orthographic features, such as emoticons, expressive lengthening, and non-standard punctuation, have become popular in social media services including Twitter and Instagram. Recently, emojis have been introduced to social media, and are increasingly popular. This r...
Perceptual causality in children.
Schlottmann, Anne; Allen, Deborah; Linderoth, Carina; Hesketh, Sarah
2002-01-01
Three experiments considered the development of perceptual causality in children from 3 to 9 years of age (N = 176 in total). Adults tend to see cause and effect even in schematic, two-dimensional motion events: Thus, if square A moves toward B, which moves upon contact, they report that A launches B--physical causality. If B moves before contact, adults report that B tries to escape from A--social or psychological causality. A brief pause between movements eliminates such impressions. Even infants in the first year of life are sensitive to causal structure in both contact and no-contact events, but previous research with talking-age children found poor verbal reports. The present experiments used a picture-based forced-choice task to reduce linguistic demands. Observers saw eight different animations involving squares A and B. Events varied in whether or not these agents made contact; whether or not there was a delay at the closest point; and whether they moved rigidly or with a rhythmic, nonrigid "caterpillar" motion. Participants of all ages assigned events with contact to the physical domain and events without contact to the psychological domain. In addition, participants of all ages chose causality more often for events without delay than with delay, but these events became more distinct over the preschool range. The manipulation of agent motion had only minor and inconsistent effects across studies, even though children of all ages considered only the nonrigid motion to be animal-like. These results agree with the view that perceptual causality is available early in development.
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... 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...
Does self-efficacy causally influence initial smoking cessation? An experimental study.
Shadel, William G; Martino, Steven C; Setodji, Claude; Cervone, Daniel; Witkiewitz, Katie
2017-10-01
Self-efficacy has been associated with smoking cessation outcomes in many correlational research studies, but strong causal inferences are lacking. This study tested whether self-efficacy affects initial smoking cessation in a laboratory experiment, which will allow for stronger causal inferences in this domain of inquiry. Participants (n=103 motivated adult smokers) were provided with brief cessation treatment over three days in preparation for quitting on a target quit day (TQD). In addition, participants were randomized to one of two standard self-efficacy manipulations in the form of bogus feedback about their chances of quitting smoking. Participants in the Average Chances of Quitting (ACQ) condition took a computerized test and were told (falsely) that the test showed that they had the same chances of quitting as everyone else in the study. Participants in the High Chances of Quitting (HCQ) condition took the same computerized test and were told (falsely) that the test showed that they had a greater chance of quitting compared to everyone else in the study. The main outcome was whether participants were able to quit for 24h on the TQD. Results revealed that HCQ participants had a significantly greater chance of quitting smoking compared to ACQ participants. However, these effects were not attributable to changes in self-efficacy brought about by the manipulation. An exploration of other potential mediators showed that the manipulation actually influenced smoking outcome expectancies, and changes in these outcome expectancies influenced initial smoking cessation. The results highlight the conceptual and empirical challenges with manipulating self-efficacy in the smoking literature. Copyright © 2017. Published by Elsevier Ltd.
Genetic Obesity and the Risk of Atrial Fibrillation- Causal Estimates from Mendelian Randomization
Chatterjee, Neal A; Giulianini, Franco; Geelhoed, Bastiaan; Lunetta, Kathryn L; Misialek, Jeffrey R; Niemeijer, Maartje N; Rienstra, Michiel; Rose, Lynda M; Smith, Albert V; Arking, Dan E; Ellinor, Patrick T; Heeringa, Jan; Lin, Honghuang; Lubitz, Steven A; Soliman, Elsayed Z; Verweij, Niek; Alonso, Alvaro; Benjamin, Emelia J; Gudnason, Vilmundur; Stricker, Bruno H; van der Harst, Pim; Chasman, Daniel I; Albert, Christine M
2017-01-01
BACKGROUND: -Observational studies have identified an association between body mass index (BMI) and incident atrial fibrillation (AF). Inferring causality from observational studies, however, is subject to residual confounding, reverse causation, and bias. The primary objective of this study was to
Rohatgi, Vijay K
2003-01-01
Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth
A Complex Systems Approach to Causal Discovery in Psychiatry.
Directory of Open Access Journals (Sweden)
Glenn N Saxe
Full Text Available Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study. Next, it was applied to a much larger dataset of traumatized children (replication study. Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment. The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro and high-level (macro insights and thus represents a promising approach for complex systems-oriented research in psychiatry.
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
Explaining through causal mechanisms
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
Enhancing scientific reasoning by refining students' models of multivariable causality
Keselman, Alla
Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained
Causality and Causal Inference in Social Work: Quantitative and Qualitative Perspectives
Palinkas, Lawrence A.
2014-01-01
Achieving the goals of social work requires matching a specific solution to a specific problem. Understanding why the problem exists and why the solution should work requires a consideration of cause and effect. However, it is unclear whether it is desirable for social workers to identify cause and effect, whether it is possible for social workers…
Causal Learning in Gambling Disorder: Beyond the Illusion of Control.
Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés
2017-06-01
Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.
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.)
Operator ordering and causality
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.
Stronger Schrödinger-like uncertainty relations
International Nuclear Information System (INIS)
Song, Qiu-Cheng; Qiao, Cong-Feng
2016-01-01
Highlights: • A stronger Schrödinger-like uncertainty relation in the sum of variances of two observables is obtained. • An improved Schrödinger-like uncertainty relation in the product of variances of two observables is obtained. • A stronger uncertainty relation in the sum of variances of three observables is proposed. - Abstract: Uncertainty relation is one of the fundamental building blocks of quantum theory. Nevertheless, the traditional uncertainty relations do not fully capture the concept of incompatible observables. Here we present a stronger Schrödinger-like uncertainty relation, which is stronger than the relation recently derived by Maccone and Pati (2014) [11]. Furthermore, we give an additive uncertainty relation which holds for three incompatible observables, which is stronger than the relation newly obtained by Kechrimparis and Weigert (2014) [12] and the simple extension of the Schrödinger uncertainty relation.
International Nuclear Information System (INIS)
Suppes, P.; Zanotti, M.
1984-01-01
This paper is concerned with inferences from phenomenological variables to hidden causes or hidden variables. A number of theorems of a general sort are stated. The paper concludes with a treatment of Bell's inequalities and their generalization to more than four observables. (Auth.)
Formalizing Neurath's ship: Approximate algorithms for online causal learning.
Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A
2017-04-01
Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Wissner-Gross, A. D.; Freer, C. E.
2013-04-01
Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.
Inference generation and story comprehension among children with ADHD.
Van Neste, Jessica; Hayden, Angela; Lorch, Elizabeth P; Milich, Richard
2015-02-01
Academic difficulties are well-documented among children with ADHD. Exploring these difficulties through story comprehension research has revealed deficits among children with ADHD in making causal connections between events and in using causal structure and thematic importance to guide recall of stories. Important to theories of story comprehension and implied in these deficits is the ability to make inferences. Often, characters' goals are implicit and explanations of events must be inferred. The purpose of the present study was to compare the inferences generated during story comprehension by 23 7- to 11-year-old children with ADHD (16 males) and 35 comparison peers (19 males). Children watched two televised stories, each paused at five points. In the experimental condition, at each pause children told what they were thinking about the story, whereas in the control condition no responses were made during pauses. After viewing, children recalled the story. Several types of inferences and inference plausibility were coded. Children with ADHD generated fewer of the most essential inferences, plausible explanatory inferences, than did comparison children, both during story processing and during story recall. The groups did not differ on production of other types of inferences. Group differences in generating inferences during the think-aloud task significantly mediated group differences in patterns of recall. Both groups recalled more of the most important story information after completing the think-aloud task. Generating fewer explanatory inferences has important implications for story comprehension deficits in children with ADHD.
Assessing statistical significance in causal graphs.
Chindelevitch, Leonid; Loh, Po-Ru; Enayetallah, Ahmed; Berger, Bonnie; Ziemek, Daniel
2012-02-20
Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing. First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs
Causal Reasoning with Mental Models
2014-08-08
mreasoner/. 445 In broad terms, three strands of evidence corroborate the model theory of causal deductions. The 446 first strand of evidence bears ...models and causal reasoning Sangeet Khemlani et al. 13 She will not gain weight. 459 Will she not eat protein? 460 The results therefore bear out the... Adele Goldberg, Catrinel Haught, Max Lotstein, Marco Ragni, and Greg 821 Trafton for helpful criticisms. 822 Khemlani et al. Causal reasoning with
Experimental evidence for circular inference in schizophrenia
Jardri, Renaud; Duverne, Sandrine; Litvinova, Alexandra S.; Denève, Sophie
2017-01-01
Schizophrenia (SCZ) is a complex mental disorder that may result in some combination of hallucinations, delusions and disorganized thinking. Here SCZ patients and healthy controls (CTLs) report their level of confidence on a forced-choice task that manipulated the strength of sensory evidence and prior information. Neither group's responses can be explained by simple Bayesian inference. Rather, individual responses are best captured by a model with different degrees of circular inference. Circular inference refers to a corruption of sensory data by prior information and vice versa, leading us to `see what we expect' (through descending loops), to `expect what we see' (through ascending loops) or both. Ascending loops are stronger for SCZ than CTLs and correlate with the severity of positive symptoms. Descending loops correlate with the severity of negative symptoms. Both loops correlate with disorganized symptoms. The findings suggest that circular inference might mediate the clinical manifestations of SCZ.
International Nuclear Information System (INIS)
Johnston, Steven
2009-01-01
We describe a quantum mechanical model for particle propagation on a causal set. The model involves calculating a particle propagator by summing amplitudes assigned to trajectories within the causal set. This 'discrete path integral' is calculated using a matrix geometric series. Amplitudes are given which, when the causal set is generated by sprinkling points into 1+1 or 3+1 Minkowski spacetime, ensure the particle propagator agrees in a suitable sense, with the retarded causal propagator for the Klein-Gordon equation.
Causality Statistical Perspectives and Applications
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
Structural Equations and Causal Explanations: Some Challenges for Causal SEM
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…
Causal Analysis for Performance Modeling of Computer Programs
Directory of Open Access Journals (Sweden)
Jan Lemeire
2007-01-01
Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.
Nonparametric causal effects based on incremental propensity score interventions
Kennedy, Edward H.
2017-01-01
Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to non-identification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental ...
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
Expert Causal Reasoning and Explanation.
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…
Introduction to causal dynamical triangulations
DEFF Research Database (Denmark)
Görlich, Andrzej
2013-01-01
The method of causal dynamical triangulations is a non-perturbative and background-independent approach to quantum theory of gravity. In this review we present recent results obtained within the four dimensional model of causal dynamical triangulations. We describe the phase structure of the mode...
Covariation in Natural Causal Induction.
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)
Paradoxical Behavior of Granger Causality
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
Women's political participation leads to stronger local economies ...
International Development Research Centre (IDRC) Digital Library (Canada)
Edgard Rodriguez - IDRC. Women attend a self-help group meeting near Hyderabad, India. Keenara Khanderia. Under changes to India's constitution, Indian women are gaining a stronger political voice. Legal reforms are encouraging women to contribute to economic growth and investments in community growth.
A Stronger Reason for the Right to Sign Languages
Trovato, Sara
2013-01-01
Is the right to sign language only the right to a minority language? Holding a capability (not a disability) approach, and building on the psycholinguistic literature on sign language acquisition, I make the point that this right is of a stronger nature, since only sign languages can guarantee that each deaf child will properly develop the…
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
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.)
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.
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.
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
Discrete causal theory emergent spacetime and the causal metric hypothesis
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.
Functional equations with causal operators
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.
Consciousness and the "Causal Paradox"
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...
Design Issues and Inference in Experimental L2 Research
Hudson, Thom; Llosa, Lorena
2015-01-01
Explicit attention to research design issues is essential in experimental second language (L2) research. Too often, however, such careful attention is not paid. This article examines some of the issues surrounding experimental L2 research and its relationships to causal inferences. It discusses the place of research questions and hypotheses,…
Neural Correlates of Bridging Inferences and Coherence Processing
Kim, Sung-il; Yoon, Misun; Kim, Wonsik; Lee, Sunyoung; Kang, Eunjoo
2012-01-01
We explored the neural correlates of bridging inferences and coherence processing during story comprehension using Positron Emission Tomography (PET). Ten healthy right-handed volunteers were visually presented three types of stories (Strong Coherence, Weak Coherence, and Control) consisted of three sentences. The causal connectedness among…
de Goeij, Moniek C M; Harting, Janneke; Kunst, Anton E
2017-03-02
Little detailed evidence is available on how integrated policies could impact population health and under what conditions such policies could be realized. The aim of this study was to assess how youth alcohol consumption trends in the province of Noord-Brabant, The Netherlands, were related to the development and implementation of integrated policies. In a retrospective multiple case study, alcohol policies of six municipalities with stronger declines in youth alcohol consumption between 2007 and 2011 (cases) were compared to four municipalities with weaker declines (controls). Information on the policy process in the same period was obtained through semi-structured in-depth interviews with policy advisors. Information on implemented interventions was extracted from policy documents and checked by the interviewees. Interviews were analyzed for thematic content. Only municipalities with stronger declines in alcohol consumption involved sectors other than public health and had started to implement interventions that use regulatory or enforcement strategies. Their involvement was facilitated by framing youth alcohol consumption as a safety rather than a health problem, whereby local media played a substantial role. Implementation of integrated policies was further facilitated by dedicated leadership and sufficient resources. Reductions in youth alcohol consumption in Noord-Brabant were stronger when municipalities started to develop integrated policies. Results suggest that integrated policies framing a health problem as a broader societal problem could positively influence population health.
The power of possibility: causal learning, counterfactual reasoning, and pretend play.
Buchsbaum, Daphna; Bridgers, Sophie; Skolnick Weisberg, Deena; Gopnik, Alison
2012-08-05
We argue for a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms, specifically the use of causal models and Bayesian learning. We suggest that exploratory childhood learning, childhood play in particular, and causal cognition are closely connected. We report an empirical study demonstrating one such connection--a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretence were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that, during human evolution, computations that were initially reserved for solving particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.
The right of the stronger: The play Sisyphus and critias
Directory of Open Access Journals (Sweden)
Jordović Ivan
2004-01-01
Full Text Available The Focus of this study is the standpoint of the play Sisyphus and critias the leader of the thirty towards the right of the stronger. this is a question of constant interest in scientific circles, since its answer can serve as the indicator of the influence this famous theory has had. this interest has been encouraged by the fact that critias’ authorship of the play is questionable. however, the question of the author is not of primary importance for this article, because there are some arguments, among some well known ones, which were not considered and which Show that in this satire, regardless of the author and the purpose of this fragment, the right of the stronger is actually non-existant. the first argument to support this theory is that nomosphysis antithesis is nowhere explicitly mentioned although it is the crucial element of the right of the stronger. in addition there is no claim in the play that the exploitation of the strong by the week or by law accrued. the second argument is that despite the incapability of laws to prevent the secret injustice, they and their importance for the human society are depicted in a positive light. it should also be noted that, unlike callicles and glaucon, laws are created to stop the bad and not the good. the third argument is that the invention of religion is accepted as a positive achievement, which finally enables the overcoming of primeval times and lawlessness. the reflection of this argument is a positive characterization of the individual who invented the fear of gods. the fourth argument, which has not been taken into consideration so far is the way the supporters and opponents of lawlessness are described and marked as κακοί and έσξλοί in the satire only physically strong are considered as strong as opposed to callicles, where they are also spiritually superior. intelectually superior in Sisyphus is the inventor of the fear of gods who is also in favor of law and order. the fact
Stronger misdirection in curved than in straight motion
Directory of Open Access Journals (Sweden)
Jorge eOtero-Millan
2011-11-01
Full Text Available Illusions developed by magicians are a rich and largely untapped source of insight into perception and cognition. Here we show that curved motion, as employed by the magician in a classic sleight of hand trick, generates stronger misdirection than rectilinear motion, and that this difference can be explained by the differential engagement of the smooth pursuit and the saccadic oculomotor systems. This research moreover exemplifies how the magician’s intuitive understanding of the spectator’s mindset can surpass that of the cognitive scientist in specific instances, and that observation-based behavioral insights developed by magicians are worthy of quantitative investigation in the neuroscience laboratory.
Rumor Mongering and Remembering: How Rumors Originating in Children's Inferences Can Affect Memory
Principe, Gabrielle F.; Guiliano, Stephanie; Root, Courtney
2008-01-01
This study examined how rumors originating in 3- to 6-year-olds' causal inferences can affect their own and their peers' memories for a personally experienced event. This was accomplished by exposing some members of classrooms to contextual clues that were designed to induce inferences about the causes of two unresolved components of the event.…
Cointegration and causal linkages in fertilizer markets across different regimes
Lahmiri, Salim
2017-04-01
Cointegration and causal linkages among five different fertilizer markets are investigated during low and high market regimes. The database includes prices of rock phosphate (RP), triple super phosphate (TSP), diammonium phosphate (DAP), urea, and potassium chloride (PC). It is found that fertilizer markets are closely linked to each other during low and high regimes; and, particularly during high regime (after 2007 international financial crisis). In addition, there is no evidence of bidirectional linear relationship between markets during low and high regime time periods. Furthermore, all significant linkages are only unidirectional. Moreover, some causality effects have emerged during high regime. Finally, the effect of an impulse during high regime time period persists longer and is stronger than the effect of an impulse during low regime time period (before 2007 international financial crisis).
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
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
Timing and causality in the generation of learned eyelid responses
Directory of Open Access Journals (Sweden)
Raudel eSánchez-Campusano
2011-08-01
Full Text Available The cerebellum-red nucleus-facial motoneuron (Mn pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP neurons and orbicularis oculi (OO Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing to longer-lasting ranges (interval timing expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and premotor spike-timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar-Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IP neurons may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality than to their generation and/or initiation.
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
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.
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.
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.
Quantum theory and local causality
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.
DEFF Research Database (Denmark)
Andersen, Jesper
2009-01-01
Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....
Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters
Weisberg, Deena S.; Gopnik, Alison
2013-01-01
Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…
Causal feedbacks in climate change
Nes, van E.H.; Scheffer, M.; Brovkin, V.; Lenton, T.M.; Ye, H.; Deyle, E.; Sugihara, G.
2015-01-01
The statistical association between temperature and greenhouse gases over glacial cycles is well documented1, but causality behind this correlation remains difficult to extract directly from the data. A time lag of CO2 behind Antarctic temperature—originally thought to hint at a driving role for
Directory of Open Access Journals (Sweden)
Richard D Stevens
Full Text Available Inference involving diversity gradients typically is gathered by mechanistic tests involving single dimensions of biodiversity such as species richness. Nonetheless, because traits such as geographic range size, trophic status or phenotypic characteristics are tied to a particular species, mechanistic effects driving broad diversity patterns should manifest across numerous dimensions of biodiversity. We develop an approach of stronger inference based on numerous dimensions of biodiversity and apply it to evaluate one such putative mechanism: the mid-domain effect (MDE. Species composition of 10,000-km(2 grid cells was determined by overlaying geographic range maps of 133 noctilionoid bat taxa. We determined empirical diversity gradients in the Neotropics by calculating species richness and three indices each of phylogenetic, functional and phenetic diversity for each grid cell. We also created 1,000 simulated gradients of each examined metric of biodiversity based on a MDE model to estimate patterns expected if species distributions were randomly placed within the Neotropics. For each simulation run, we regressed the observed gradient onto the MDE-expected gradient. If a MDE drives empirical gradients, then coefficients of determination from such an analysis should be high, the intercept no different from zero and the slope no different than unity. Species richness gradients predicted by the MDE fit empirical patterns. The MDE produced strong spatially structured gradients of taxonomic, phylogenetic, functional and phenetic diversity. Nonetheless, expected values generated from the MDE for most dimensions of biodiversity exhibited poor fit to most empirical patterns. The MDE cannot account for most empirical patterns of biodiversity. Fuller understanding of latitudinal gradients will come from simultaneous examination of relative effects of random, environmental and historical mechanisms to better understand distribution and abundance of the
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
Energy Technology Data Exchange (ETDEWEB)
Petrov, S.
1996-10-01
Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
Geometric statistical inference
International Nuclear Information System (INIS)
Periwal, Vipul
1999-01-01
A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined
Bailer-Jones, Coryn A. L.
2017-04-01
Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.
A new test of multivariate nonlinear causality.
Bai, Zhidong; Hui, Yongchang; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong
2018-01-01
The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.
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.
Conservatives Anticipate and Experience Stronger Emotional Reactions to Negative Outcomes.
Joel, Samantha; Burton, Caitlin M; Plaks, Jason E
2014-02-01
The present work examined whether conservatives and liberals differ in their anticipation of their own emotional reactions to negative events. In two studies, participants imagined experiencing positive or negative outcomes in domains that do not directly concern politics. In Study 1, 190 American participants recruited online (64 male, Mage = 32 years) anticipated their emotional responses to romantic relationship outcomes. In Study 2, 97 Canadian undergraduate students (26 male, Mage = 21 years) reported on their anticipated and experienced emotional responses to academic outcomes. In both studies, more conservative participants predicted they would feel stronger negative emotions following negative outcomes than did more liberal participants. Furthermore, a longitudinal follow-up of Study 2 participants revealed that more conservative participants actually felt worse than more liberal participants after receiving a lower-than-desired exam grade. These effects remained even when controlling for the Big Five traits, prevention focus, and attachment style (Study 1), and optimism (Study 2). We discuss how the relationship between political orientation and anticipated affect likely contributes to differences between conservatives and liberals in styles of decision and policy choices. © 2013 Wiley Periodicals, Inc.
Stronger inducible defences enhance persistence of intraguild prey.
Kratina, Pavel; Hammill, Edd; Anholt, Bradley R
2010-09-01
1. Intraguild predation is widespread in nature despite its potentially destabilizing effect on food web dynamics. 2. Anti-predator inducible defences affect both birth and death rates of populations and have the potential to substantially modify food web dynamics and possibly increase persistence of intraguild prey. 3. In a chemostat experiment, we investigated the long-term effects of inducible defences on the dynamics of aquatic microbial food webs consisting of an intraguild predator, intraguild prey, and a basal resource. We controlled environmental conditions and selected strains of intraguild prey that varied in the strength of expressed inducible defences. 4. We found that intraguild prey with a stronger tendency to induce an anti-predator morphology persist for significantly longer periods of time. In addition, model selection analysis implied that flexibility in defensive phenotype (inducibility itself) is most likely the factor responsible for the enhanced persistence. 5. As patterns at the community level often emerge as a result of the life-history traits of individuals, we propose that inducible defences increase the persistence of populations and may contribute to the widespread occurrence of theoretically unstable intraguild predation systems in nature.
Causally-Rich Group Play: A Powerful Context for Building Preschoolers' Vocabulary.
Bauer, Jessie Raye; Booth, Amy E; McGroarty-Torres, Kathleen
2016-01-01
This work explores whether the facilitative effect of causal information on preschoolers' word learning observed in the laboratory might be relevant to boosting children's vocabulary in a group-play context. Forty-eight 3- to 4-year-old children learned six novel words for novel tools introduced during a small group-play session. Half of the groups used the tools according to their specified function to construct a fruit salad. The remaining children used the same tools to decorate a castle of blocks. In this way, some children learned about the causal properties of the tools, while others did not. Although children in both conditions comprehended the novel words equally well when tested shortly after the play session, learning in the Causal condition was more robust. Children's comprehension scores in the Causal condition increased over time (a 7-20 day delay), such that children in this group performed better than children in the Non-Causal condition when tested in a follow-up session. These results demonstrate a striking benefit of causal enrichment to word learning in a context that could feasibly be implemented in preschool classrooms, playgroups, and individual households. Highlighting the causal properties of objects during playtime might offer a powerful approach to building children's vocabulary, thereby providing a stronger foundation for early literacy and success in school more generally speaking.
PREMER: a Tool to Infer Biological Networks.
Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R
2017-10-04
Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).
Two roads to noncommutative causality
International Nuclear Information System (INIS)
Besnard, Fabien
2015-01-01
We review the physical motivations and the mathematical results obtained so far in the isocone-based approach to noncommutative causality. We also give a briefer account of the alternative framework of Franco and Eckstein which is based on Lorentzian spectral triples. We compare the two theories on the simple example of the product geometry of the Minkowski plane by the finite noncommutative space with algebra M 2 (C). (paper)
Reinforcement learning or active inference?
Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J
2009-07-29
This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Reinforcement learning or active inference?
Directory of Open Access Journals (Sweden)
Karl J Friston
2009-07-01
Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Nagao, Makoto
1990-01-01
Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig
Logical inference and evaluation
International Nuclear Information System (INIS)
Perey, F.G.
1981-01-01
Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given
Concept of statistical causality and local martingales
Directory of Open Access Journals (Sweden)
Valjarević Dragana
2016-01-01
Full Text Available In this paper we consider a statistical concept of causality in continuous time in filtered probability spaces which is based on Granger's definitions of causality. The given causality concept is closely connected to the preservation of the property being a local martingale if the filtration is getting larger. Namely, the local martingale remains unpredictable if the amount of information is increased. We proved that the preservation of this property is equivalent with the concept of causality.
Obesity and infection: reciprocal causality.
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.
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 ...
Causal knowledge and reasoning in decision making
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
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....
Probability and Statistical Inference
Prosper, Harrison B.
2006-01-01
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.
Normative and descriptive accounts of the influence of power and contingency on causal judgement.
Perales, José C; Shanks, David R
2003-08-01
The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.
Is Polar Amplification Deeper and Stronger than Dynamicists Assume?
Scheff, J.; Maroon, E.
2017-12-01
In the CMIP multi-model mean under strong future warming, Arctic amplification is confined to the lower troposphere, so that the meridional gradient of warming reverses around 500 mb and the upper troposphere is characterized by strong "tropical amplification" in which warming weakens with increasing latitude. This model-derived pattern of warming maxima in the upper-level tropics and lower-level Arctic has become a canonical assumption driving theories of the large-scale circulation response to climate change. Yet, several lines of evidence and reasoning suggest that Arctic amplification may in fact extend through the entire depth of the troposphere, and/or may be stronger than commonly modeled. These include satellite Microwave Sounding Unit (MSU) temperature trends as a function of latitude and vertical level, the recent discovery that the extratropical negative cloud phase feedback in models is largely spurious, and the very strong polar amplification observed in past warm and lukewarm climates. Such a warming pattern, with deep, dominant Arctic amplification, would have very different implications for the circulation than a canonical CMIP-like warming: instead of slightly shifting poleward and strengthening, eddies, jets and cells might shift equatorward and considerably weaken. Indeed, surface winds have been mysteriously weakening ("stilling") at almost all stations over the last half-century or so, there has been no poleward shift in northern hemisphere circulation metrics, and past warm climates' subtropics were apparently quite wet (and their global ocean circulations were weak.) To explore these possibilities more deeply, we examine the y-z structure of warming and circulation changes across a much broader range of models, scenarios and time periods than the CMIP future mean, and use an MSU simulator to compare them to the satellite warming record. Specifically, we examine whether the use of historical (rather than future) forcing, AMIP (rather than CMIP
Increasing Arctic sea ice export driven by stronger winds
Sorteberg, A.; Smedsrud, L. H.; Sirevaag, A.; Kloster, K.
2010-12-01
Arctic sea ice area has decreased steadily over the last three decades. A thinner and more seasonal Arctic ice cover, related to increased long wave radiation, has become evident. Changes in circulation, including drift patterns of the Arctic pack ice, have been less obvious. Arctic sea ice export estimates have been hampered by low resolution spatial and temporal satellite imagery, especially during summer, making accurate detection difficult. Here we present a new ice area export dataset calculated from sea ice motion and concentration profiles along 79N. Ice drift vectors are calculated from ice feature displacement using Envisat ASAR WideSwath images every 3 days from 2004 while ice concentration is based on DMSP F13 SSMI and AQUA AMSR-E brightness temperature data. The two data sets are combined to give the ice-area flux in consecutive 3-day periods, uninterrupted year-round coverage along 79N. It is shown that sea ice export variability is closely linked to the geostrophic wind in the Fram Strait (correlation of 0.84). Using geostrophic winds from reanalysis back to the 1950s as a proxy for ice export indicates that the Arctic sea ice has annually lost an increasing area since the 1950's driven by stronger winds. Ice concentration has decreased slightly, but does not contribute significantly. The ice export has overall increased by ~25% over the period. Using cyclone tracking the changes in winds seems directly related to a higher low pressure activity in the Nordic Seas. Our results demonstrate that the changes in atmospheric circulation over the Arctic and sub-Arctic have contributed to a trend in the Fram Strait ice export. The Fram Strait between Greenland and Svalbard with average sea ice concentration for summer (red, June through August) and winter (black, January through March). Solid lines are 50%, dashed lines are 15%. Above mean southward ice drift across 79N from August 2004 to July 2010 in 1 degree bins based on SAR imagery, and mean ice
Introductory statistical inference
Mukhopadhyay, Nitis
2014-01-01
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist
Norms and customs: causally important or causally impotent?
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.
Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.
Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan
2017-05-01
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*
Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan
2017-01-01
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111
Penfold, Christopher A; Shifaz, Ahmed; Brown, Paul E; Nicholson, Ann; Wild, David L
2015-06-01
Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.
Encouraging children to think counterfactually enhances blocking in a causal learning task.
McCormack, Teresa; Simms, Victoria; McGourty, Jemma; Beckers, Tom
2013-01-01
According to a higher order reasoning account, inferential reasoning processes underpin the widely observed cue competition effect of blocking in causal learning. The inference required for blocking has been described as modus tollens (if p then q, not q therefore not p). Young children are known to have difficulties with this type of inference, but research with adults suggests that this inference is easier if participants think counterfactually. In this study, 100 children (51 five-year-olds and 49 six- to seven-year-olds) were assigned to two types of pretraining groups. The counterfactual group observed demonstrations of cues paired with outcomes and answered questions about what the outcome would have been if the causal status of cues had been different, whereas the factual group answered factual questions about the same demonstrations. Children then completed a causal learning task. Counterfactual pretraining enhanced levels of blocking as well as modus tollens reasoning but only for the younger children. These findings provide new evidence for an important role for inferential reasoning in causal learning.
MMI: Multimodel inference or models with management implications?
Fieberg, J.; Johnson, Douglas H.
2015-01-01
We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
One Year After Fukushima, Nuclear Safety Is Stronger
International Nuclear Information System (INIS)
2012-01-01
Full text: Nuclear power is safer than it was a year ago as the nuclear industry, regulators and governments act on the lessons of Fukushima, but that safety must never be taken for granted, said Yukiya Amano, Director General of the International Atomic Energy Agency (IAEA). Speaking ahead of the first anniversary of the Fukushima Daiichi nuclear accident on 11 March, Amano said a culture of constant vigilance and improvement was vital to ensure that the benefits of nuclear power could be harnessed as safely as humanly possible. 'Nuclear safety is stronger than it was a year ago', he said. 'Fukushima Daiichi was a very serious accident, but we know what went wrong and we have a clear course of action to tackle those causes - not only in Japan, but anywhere in the world. 'Now we have to keep up the momentum. Complacency can kill'. On 11 March 2011 a huge earthquake and tsunami left more than 20 000 people dead or missing in eastern Japan. Amidst widespread destruction, the tsunami slammed into the Fukushima Daiichi Nuclear Power Station, disabling cooling systems and leading to fuel meltdowns in three of the six Units. The accident was a jolt to the nuclear industry, regulators and governments. It was triggered by a massive force of nature, but it was existing weaknesses of design regarding defence against natural hazards, regulatory oversight, accident management and emergency response that allowed it to unfold as it did. For example: The nuclear regulator was not sufficiently independent, allowing weak oversight of the operator, TEPCO, and regulatory requirements fell short of international best practice; Not enough attention was paid to guarding against possible extreme events at the Fukushima Daiichi site, leaving critical safety functions such as cooling systems vulnerable to the tsunami; Training to respond to serious accidents was inadequate, as were mitigation measures to prevent hydrogen explosions and protect the venting system; and Accident command lines
Identifying Causality from Alarm Observations
DEFF Research Database (Denmark)
Kirchhübel, Denis; Zhang, Xinxin; Lind, Morten
on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision...... support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes...
Random number generators and causality
International Nuclear Information System (INIS)
Larrondo, H.A.; Martin, M.T.; Gonzalez, C.M.; Plastino, A.; Rosso, O.A.
2006-01-01
We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG's) that do not pass Marsaglia's DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG's are evaluated and the associated results are compared to those obtained by recourse to Marsaglia's DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test
CADDIS Volume 1. Stressor Identification: About Causal Assessment
An introduction to the history of our approach to causal assessment, A chronology of causal history and philosophy, An introduction to causal history and philosophy, References for the Causal Assessment Background section of Stressor Identification
Stolzenburg, Susanne; Freitag, Simone; Schmidt, Silke; Schomerus, Georg
2017-11-06
Past research has shown that among the general public, certain causal explanations like biomedical causes are associated with stronger desire for social distance from persons with mental illness. Aim of this study was to find out how different causal attributions of persons with untreated mental health problems regarding their own complaints are associated with stigmatizing attitudes, anticipated self-stigma when seeking help and perceived stigma-stress. Altogether, 207 untreated persons with a current depressive syndrome were interviewed. Biomedical causes, but also belief in childhood trauma or unhealthy behavior as a cause of the problem, were associated with stronger personal stigma and with more stigma-stress. Similarities and differences to findings among the general population and implications for future research are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.
Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle
Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen
2017-04-01
Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.
Linear and nonlinear causality between renewable energy consumption and economic growth in the USA
Directory of Open Access Journals (Sweden)
Haiyun Xu
2016-12-01
Full Text Available This study aims to investigate Granger causality between renewable energy consumption (REC and economic growth (EG for USA. To accomplish this objective and to add the stronger evidence to the controversial issue, the tests were done under a new framework that embeds wavelet analysis, a novel tool, in nonlinear causality test approaches developed recently. The classical linear causality test procedure was also involved for comparison. The empirical data sources from the USA Energy Information Administration and Economist Intelligence Unit (EIU CountryData database. Sample period is from January 1993 to October 2014. The results indicate significantly the existence of unidirectional causality from EG to REC and support the conservation hypothesis. In additional, further evidences show that the causal relationship among them is not constant and depends on the time scale or frequency ranges, and that wavelet analysis is an important aid to capture the nonlinear causality. This suggests that renewable energy limitations do not seem to damage economic growth. These results have implications of importance for research analysts as well as policy makers of energy economy.
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
BioCause: Annotating and analysing causality in the biomedical domain.
Mihăilă, Claudiu; Ohta, Tomoko; Pyysalo, Sampo; Ananiadou, Sophia
2013-01-16
Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining. We have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for the training of automatic causality detection systems. Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new
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.
Assessment of network inference methods: how to cope with an underdetermined problem.
Directory of Open Access Journals (Sweden)
Caroline Siegenthaler
Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.
Reducing Children’s Behavior Problems through Social Capital: A Causal Assessment
López Turley, Ruth N.; Gamoran, Adam; McCarty, Alyn Turner; Fish, Rachel
2016-01-01
Behavior problems among young children have serious detrimental effects on short and long-term educational outcomes. An especially promising prevention strategy may be one that focuses on strengthening the relationships among families in schools, or social capital. However, empirical research on social capital has been constrained by conceptual and causal ambiguity. This study attempts to construct a more focused conceptualization of social capital and aims to determine the causal effects of social capital on children’s behavior. Using data from a cluster randomized trial of 52 elementary schools, we apply several multilevel models to assess the causal relationship, including intent to treat and treatment on the treated analyses. Taken together, these analyses provide stronger evidence than previous studies that social capital improves children’s behavioral outcomes and that these improvements are not simply a result of selection into social relations but result from the social relations themselves. PMID:27886729
Kampen, Jarl K.
2011-06-01
At best, the empirical evidence for human impact on climate change, more specifically, the anthropogenic global warming (AGW), is based on correlational research. That is, no experiment has been carried out that confirms or falsifies the causal hypothesis put forward by the International Panel on Climate Change (IPCC) that anthropogenic increasing of green house gas concentrations very likely causes increasing of the (mean) global temperature. In this article, we point out the major weaknesses of correlational research in assessing causal hypotheses. We further point out that the AGW hypothesis is in need of potential falsifiers in the Popperian (neopositivistic) sense. Some directions for future research on the formulation of such falsifiers in causal research are discussed. Of course, failure to find falsifying evidence in empirical climate data will render the AWG hypothesis much stronger.
A Note on Causal Relationship between FDI and Savings in Bangladesh
Directory of Open Access Journals (Sweden)
Mohammad SALAHUDDIN
2010-11-01
Full Text Available This paper aims to investigate the causal relationship between foreign direct investment and gross domestic savings in Bangladesh over a period of 1985-2007. In doing so, Johansen cointegration technique and error correction methods are employed to examine the long run and short run relationship between foreign direct investment and gross domestic savings. To determine the direction of causality, we used innovation accounting approach. Results suggest that there exist bi-directional causal relationship between foreign direct investment and gross domestic savings but the movement is stronger from domestic savings to foreign direct investment. The result also implies complimentary relationship between them and as such, policy makers in Bangladesh need to focus on the determinants of both FDI and domestic savings in order to accelerate its growth.
Linear causal modeling with structural equations
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
Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection.
Park, Eun-Hyoung; Madsen, Joseph R
2018-01-01
A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if "causality" in the Granger sense correlated with surgical targets. To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection. Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The "GC maps" were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics. In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P aggregate probability of such a match by chance alone is very small (P < 10-20) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001). GC analysis has the potential to help localize ictal networks from interictal data. © Congress of Neurological Surgeons 2017.
Electromagnetic pulses, localized and causal
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.
Making Type Inference Practical
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens
1992-01-01
We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. A......-oriented languages practical....
Type Inference with Inequalities
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff
1991-01-01
of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both...
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
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
Tools for Detecting Causality in Space Systems
Johnson, J.; Wing, S.
2017-12-01
Complex systems such as the solar and magnetospheric envivonment often exhibit patterns of behavior that suggest underlying organizing principles. Causality is a key organizing principle that is particularly difficult to establish in strongly coupled nonlinear systems, but essential for understanding and modeling the behavior of systems. While traditional methods of time-series analysis can identify linear correlations, they do not adequately quantify the distinction between causal and coincidental dependence. We discuss tools for detecting causality including: granger causality, transfer entropy, conditional redundancy, and convergent cross maps. The tools are illustrated by applications to magnetospheric and solar physics including radiation belt, Dst (a magnetospheric state variable), substorm, and solar cycle dynamics.
Does causal action facilitate causal perception in infants younger than 6 months of age?
Rakison, David H; Krogh, Lauren
2012-01-01
Previous research has established that infants are unable to perceive causality until 6¼ months of age. The current experiments examined whether infants' ability to engage in causal action could facilitate causal perception prior to this age. In Experiment 1, 4½-month-olds were randomly assigned to engage in causal action experience via Velcro sticky mittens or not engage in causal action because they wore non-sticky mittens. Both groups were then tested in the visual habituation paradigm to assess their causal perception. Infants who engaged in causal action - but not those without this causal action experience - perceived the habituation events as causal. Experiment 2 used a similar design to establish that 4½-month-olds are unable to generalize their own causal action to causality observed in dissimilar objects. These data are the first to demonstrate that infants under 6 months of age can perceive causality, and have implications for the mechanisms underlying the development of causal perception. © 2011 Blackwell Publishing Ltd.
Inferring human intentions from the brain data
DEFF Research Database (Denmark)
Stanek, Konrad
discharges across the neural tissue are responsible for emergence of high cognitive function, conscious perception and voluntary action. The brain’s capacity to exercise free will, or internally generated free choice, has long been investigated by philosophers, psychologists and neuroscientists. Rather than......The human brain is a massively complex organ composed of approximately a hundred billion densely interconnected, interacting neural cells. The neurons are not wired randomly - instead, they are organized in local functional assemblies. It is believed that the complex patterns of dynamic electric...... assuming a causal power of conscious will, the neuroscience of volition is based on the premise that "mental states rest on brain processes”, and hence by measuring spatial and temporal correlates of volition in carefully controlled experiments we can infer about their underlying mind processes, including...
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.
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.
Kelcey, Benjamin; Dong, Nianbo; Spybrook, Jessaca; Cox, Kyle
2017-01-01
Designs that facilitate inferences concerning both the total and indirect effects of a treatment potentially offer a more holistic description of interventions because they can complement "what works" questions with the comprehensive study of the causal connections implied by substantive theories. Mapping the sensitivity of designs to…
Irony as Inferred Contradiction
Directory of Open Access Journals (Sweden)
Лаура Альба-Хуес
2014-12-01
Full Text Available “If we acknowledge the existence of an Irony Principle, we should also acknowledge another ‘higher-order principle’ which has the opposite effect. While irony is an apparently friendly way of being offensive (mock politeness, the type of verbal behaviour known as ‘banter’ is an offensive way of being friendly (mock impoliteness.” Geoffrey Leech, Principles of Pragmatics (1983: 144 In this work I present some theoretical considerations about what I consider to be a permanent and ever-present feature of verbal irony, namely, inferred contradiction , which has to be distinguished from plain, direct (non-inferred contradiction as well as from indirect negation , for a contradiction which is directly expressed cannot be interpreted as ironical (since it lacks a crucial component: inference, and an indirect negation may or may not be ironic (depending on the situation, and thus cannot be considered a permanent feature of the phenomenon. In spite of the fact that many scholars have proposed different theories in order to capture the essence of this intricate and complex phenomenon, not all of them have managed to find a feature or characteristic that applies to or is found in all possible occurrences of irony. I briefly discuss the tenets of some of the best-known of these theories, namely the Classical theories (Socrates, Cicero, Quintilian, the Echoic-Mention Theory (later Echoic Theory, the Echoic Reminder Theory, the Pretence Theory and the Relevant Inappropriateness Theory, trying to show that in all the types of irony emerging from these proposals (e.g. echoic irony, pretence irony, etc. it can be observed that the irony is triggered by inferred contradiction . The one theory that according to my view and knowledge- seems to capture its whole essence to date is Attardo’s (2000 Relevant Inappropriateness Theory, to whose proposal I adhere, but I argue at the same time that inferred contradiction is another feature of irony (which
Prenatal nutrition, epigenetics and schizophrenia risk: can we test causal effects?
Kirkbride, James B; Susser, Ezra; Kundakovic, Marija; Kresovich, Jacob K; Davey Smith, George; Relton, Caroline L
2012-06-01
We posit that maternal prenatal nutrition can influence offspring schizophrenia risk via epigenetic effects. In this article, we consider evidence that prenatal nutrition is linked to epigenetic outcomes in offspring and schizophrenia in offspring, and that schizophrenia is associated with epigenetic changes. We focus upon one-carbon metabolism as a mediator of the pathway between perturbed prenatal nutrition and the subsequent risk of schizophrenia. Although post-mortem human studies demonstrate DNA methylation changes in brains of people with schizophrenia, such studies cannot establish causality. We suggest a testable hypothesis that utilizes a novel two-step Mendelian randomization approach, to test the component parts of the proposed causal pathway leading from prenatal nutritional exposure to schizophrenia. Applied here to a specific example, such an approach is applicable for wider use to strengthen causal inference of the mediating role of epigenetic factors linking exposures to health outcomes in population-based studies.
Testing for causality in covarying traits: genes and latitude in a molecular world.
O'Brien, Conor; Bradshaw, William E; Holzapfel, Christina M
2011-06-01
Many traits are assumed to have a causal (necessary) relationship with one another because of their common covariation with a physiological, ecological or geographical factor. Herein, we demonstrate a straightforward test for inferring causality using residuals from regression of the traits with the common factor. We illustrate this test using the covariation with latitude of a proxy for the circadian clock and a proxy for the photoperiodic timer in Drosophila and salmon. A negative result of this test means that further discussion of the adaptive significance of a causal connection between the covarying traits is unwarranted. A positive result of this test provides a point of departure that can then be used as a platform from which to determine experimentally the underlying functional connections and only then to discuss their adaptive significance.
Causal random geometry from stochastic quantization
DEFF Research Database (Denmark)
Ambjørn, Jan; Loll, R.; Westra, W.
2010-01-01
in this short note we review a recently found formulation of two-dimensional causal quantum gravity defined through Causal Dynamical Triangulations and stochastic quantization. This procedure enables one to extract the nonperturbative quantum Hamiltonian of the random surface model including the...... the sum over topologies. Interestingly, the generally fictitious stochastic time corresponds to proper time on the geometries...
Special Relativity, Causality and Quantum Mechanics-2
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 11; Issue 9. Special Relativity, Causality and Quantum Mechanics - 2. Guruprasad Kar Samir Kunkri Sujit K Choudhary. General Article Volume 11 Issue 9 ... Keywords. Causality; quantum entanglement; cloning; local realism; completely positive maps.
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.
Causal Mediation Analysis: Warning! Assumptions Ahead
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…
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Testing the causality of Hawkes processes with time reversal
Cordi, Marcus; Challet, Damien; Muni Toke, Ioane
2018-03-01
We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, tests of goodness of parametric fit unambiguously reject backward event times, which implies that inferring kernels from time-symmetric quantities, such as the autocovariance of the event rate, only rarely produce statistically significant fits. Finally, we find that fitting financial data with many-parameter kernels may yield significant fits for both arrows of time for the same event time vector, sometimes favouring the backward time direction. This goes to show that a significant fit of Hawkes processes to real data with flexible kernels does not imply a definite arrow of time unless one tests it.
Multiple Instance Fuzzy Inference
2015-12-02
and learn the fuzzy inference system’s parameters [24, 25]. In this later technique, supervised and unsupervised learning algorithms are devised to...algorithm ( unsupervised learning ) can be used to identify local contexts of the input space, and a linear classifier (supervised learning ) can be used...instance level (patch-level) labels and would require the image to be correctly segmented and labeled prior to learning . Figure 1.1: Example of an image
2018-02-15
whether unsupervised (such as clustering) or supervised (such as Naive Bayes). We observed the following advantages: 1 APPROVED FOR PUBLIC RELEASE...section, we explain our research in relation to DARPA’s Probabilistic Programming for Advancing Machine Learning (PPAML) program and other approaches...develop machine- learning applications by combining probabilistic models and inference techniques. On one hand, a probabilistic model is a mathematical
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...
Active inference and learning.
Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni
2016-09-01
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Continuous Integrated Invariant Inference Project
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
Leach, Prescott T; Kenney, Justin W; Gould, Thomas J
2015-02-01
Increased training often results in stronger memories but the neural changes responsible for these stronger memories are poorly understood. It is proposed here that higher levels of training that result in stronger memories recruit additional cell signaling cascades. This study specifically examined if c-Jun N-terminal kinase 1 (JNK1) is involved in the formation of stronger fear conditioning memories. Wildtype (WT), JNK1 heterozygous (Het), and JNK1 knockout (KO) mice were fear conditioned with 1 trial, 2 trials, or 4 trials. All mice learned both contextual (hippocampus-dependent) and cued (hippocampus-independent) fear conditioning but for contextual fear conditioning only, the JNK1 KO mice did not show higher levels of learning with increased trials. That is, WT mice showed a significant linear increase in contextual fear conditioning as training trials increased from 1 to 2 to 4 trials whereas KO mice showed the same level of contextual fear conditioning as WT mice for 1 trial training but did not have increased levels of contextual fear conditioning with additional trials. These data suggest that JNK1 may not be critical for learning but when higher levels of hippocampus-dependent learning occur, JNK1 signaling is recruited and is necessary for stronger hippocampus-dependent memory formation. Copyright © 2014 Elsevier Inc. All rights reserved.
Causal ubiquity in quantum physics a superluminal and local-causal physical ontology
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
Multimodel inference and adaptive management
Rehme, S.E.; Powell, L.A.; Allen, Craig R.
2011-01-01
Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.
Serang, Oliver
2014-01-01
Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called “causal independence”). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to and the space to where is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions. PMID:24626234
Biological causal links on physiological and evolutionary time scales.
Karmon, Amit; Pilpel, Yitzhak
2016-04-26
Correlation does not imply causation. If two variables, say A and B, are correlated, it could be because A causes B, or that B causes A, or because a third factor affects them both. We suggest that in many cases in biology, the causal link might be bi-directional: A causes B through a fast-acting physiological process, while B causes A through a slowly accumulating evolutionary process. Furthermore, many trained biologists tend to consistently focus at first on the fast-acting direction, and overlook the slower process in the opposite direction. We analyse several examples from modern biology that demonstrate this bias (codon usage optimality and gene expression, gene duplication and genetic dispensability, stem cell division and cancer risk, and the microbiome and host metabolism) and also discuss an example from linguistics. These examples demonstrate mutual effects between the fast physiological processes and the slow evolutionary ones. We believe that building awareness of inference biases among biologists who tend to prefer one causal direction over another could improve scientific reasoning.
Learning Wireless Networks' Topologies Using Asymmetric Granger Causality
Laghate, Mihir; Cabric, Danijela
2018-02-01
Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.
Causal systems categories: differences in novice and expert categorization of causal phenomena.
Rottman, Benjamin M; Gentner, Dedre; Goldwater, Micah B
2012-07-01
We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures. Copyright © 2012 Cognitive Science Society, Inc.
Scior, Katrina; Furnham, Adrian
2016-09-30
Evidence on mental illness stigma abounds yet little is known about public perceptions of intellectual disability. This study examined causal beliefs about intellectual disability and schizophrenia and how these relate to awareness of the condition and social distance. UK lay people aged 16+(N=1752), in response to vignettes depicting intellectual disability and schizophrenia, noted their interpretation of the difficulties, and rated their agreement with 22 causal and four social distance items. They were most likely to endorse environmental causes for intellectual disability, and biomedical factors, trauma and early disadvantage for schizophrenia. Accurate identification of both vignettes was associated with stronger endorsement of biomedical causes, alongside weaker endorsement of adversity, environmental and supernatural causes. Biomedical causal beliefs and social distance were negatively correlated for intellectual disability, but not for schizophrenia. Causal beliefs mediated the relationship between identification of the condition and social distance for both conditions. While all four types of causal beliefs acted as mediators for intellectual disability, for schizophrenia only supernatural causal beliefs did. Educating the public and promoting certain causal beliefs may be of benefit in tackling intellectual disability stigma, but for schizophrenia, other than tackling supernatural attributions, may be of little benefit in reducing stigma. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Relton, Caroline L; Davey Smith, George
2012-01-01
The burgeoning interest in the field of epigenetics has precipitated the need to develop approaches to strengthen causal inference when considering the role of epigenetic mediators of environmental exposures on disease risk. Epigenetic markers, like any other molecular biomarker, are vulnerable to confounding and reverse causation. Here, we present a strategy, based on the well-established framework of Mendelian randomization, to interrogate the causal relationships between exposure, DNA methylation and outcome. The two-step approach first uses a genetic proxy for the exposure of interest to assess the causal relationship between exposure and methylation. A second step then utilizes a genetic proxy for DNA methylation to interrogate the causal relationship between DNA methylation and outcome. The rationale, origins, methodology, advantages and limitations of this novel strategy are presented. PMID:22422451
A Simple Test for Causality in Volatility
Directory of Open Access Journals (Sweden)
Chia-Lin Chang
2017-03-01
Full Text Available An early development in testing for causality (technically, Granger non-causality in the conditional variance (or volatility associated with financial returns was the portmanteau statistic for non-causality in the variance of Cheng and Ng (1996. A subsequent development was the Lagrange Multiplier (LM test of non-causality in the conditional variance by Hafner and Herwartz (2006, who provided simulation results to show that their LM test was more powerful than the portmanteau statistic for sample sizes of 1000 and 4000 observations. While the LM test for causality proposed by Hafner and Herwartz (2006 is an interesting and useful development, it is nonetheless arbitrary. In particular, the specification on which the LM test is based does not rely on an underlying stochastic process, so the alternative hypothesis is also arbitrary, which can affect the power of the test. The purpose of the paper is to derive a simple test for causality in volatility that provides regularity conditions arising from the underlying stochastic process, namely a random coefficient autoregressive process, and a test for which the (quasi- maximum likelihood estimates have valid asymptotic properties under the null hypothesis of non-causality. The simple test is intuitively appealing as it is based on an underlying stochastic process, is sympathetic to Granger’s (1969, 1988 notion of time series predictability, is easy to implement, and has a regularity condition that is not available in the LM test.
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 logic...... underlying studies that use these “causal-temporal” concepts. The chapter shows visually how causality and temporality are linked to one another in varying ways depending on the particular pattern of change. The chapter provides new tools for describing and understanding change in historical- institutional...
Dual Causality and the Autonomy of Biology.
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.
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples.
Directory of Open Access Journals (Sweden)
Yoshito Hirata
Full Text Available Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly.
Jansen, Henning; Lieb, Wolfgang; Schunkert, Heribert
2016-02-01
Epidemiological and clinical studies have identified many physiological traits and biomarkers that are statistically associated with coronary artery disease (CAD). For some of these traits and biomarkers it is well established that they represent true causal risk factors for CAD. For other biomarkers, however, the distinct character of association is still a matter of debate. Randomized controlled trials (RCT) had a pivotal role in establishing causal associations between risk factors and biomarkers and CAD in some settings by demonstrating that therapeutic intervention targeting risk factors/biomarkers also affect the risk for clinical outcomes, such as CAD. In other scenarios, however, RCTs did not demonstrate clear benefits associated with lowering biomarker levels and therefore suggest that the association between these biomarkers (like C reactive protein) and CAD was driven by confounding or reverse causation. Even accurately conducted RCTs are not immune against incorrect causal inference. Moreover, the extensive costs and efforts required to conduct RCTs asked for alternative study designs to elucidate potential causal associations. Mendelian Randomization studies represent one such alternative by using genetic variants as proxies for specific biomarkers to investigate potential causal relations between biomarkers and clinical outcomes. In this review, we briefly describe the principles of MR studies and summarize recent MR studies in the context of CAD.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Nanotechnology and statistical inference
Vesely, Sara; Vesely, Leonardo; Vesely, Alessandro
2017-08-01
We discuss some problems that arise when applying statistical inference to data with the aim of disclosing new func-tionalities. A predictive model analyzes the data taken from experiments on a specific material to assess the likelihood that another product, with similar structure and properties, will exhibit the same functionality. It doesn't have much predictive power if vari-ability occurs as a consequence of a specific, non-linear behavior. We exemplify our discussion on some experiments with biased dice.
DEFF Research Database (Denmark)
Andersen, Jesper; Lawall, Julia
2010-01-01
A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2012-06-01
Full Text Available We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continuity nor differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of quantifying rules that form the familiar probability calculus. We also derive a unique quantification of divergence, entropy and information.
Pairwise measures of causal direction in the epidemiology of sleep problems and depression.
Directory of Open Access Journals (Sweden)
Tom Rosenström
Full Text Available Depressive mood is often preceded by sleep problems, suggesting that they increase the risk of depression. Sleep problems can also reflect prodromal symptom of depression, thus temporal precedence alone is insufficient to confirm causality. The authors applied recently introduced statistical causal-discovery algorithms that can estimate causality from cross-sectional samples in order to infer the direction of causality between the two sets of symptoms from a novel perspective. Two common-population samples were used; one from the Young Finns study (690 men and 997 women, average age 37.7 years, range 30-45, and another from the Wisconsin Longitudinal study (3101 men and 3539 women, average age 53.1 years, range 52-55. These included three depression questionnaires (two in Young Finns data and two sleep problem questionnaires. Three different causality estimates were constructed for each data set, tested in a benchmark data with a (practically known causality, and tested for assumption violations using simulated data. Causality algorithms performed well in the benchmark data and simulations, and a prediction was drawn for future empirical studies to confirm: for minor depression/dysphoria, sleep problems cause significantly more dysphoria than dysphoria causes sleep problems. The situation may change as depression becomes more severe, or more severe levels of symptoms are evaluated; also, artefacts due to severe depression being less well presented in the population data than minor depression may intervene the estimation for depression scales that emphasize severe symptoms. The findings are consistent with other emerging epidemiological and biological evidence.
Inferring horizontal gene transfer.
Directory of Open Access Journals (Sweden)
Matt Ravenhall
2015-05-01
Full Text Available Horizontal or Lateral Gene Transfer (HGT or LGT is the transmission of portions of genomic DNA between organisms through a process decoupled from vertical inheritance. In the presence of HGT events, different fragments of the genome are the result of different evolutionary histories. This can therefore complicate the investigations of evolutionary relatedness of lineages and species. Also, as HGT can bring into genomes radically different genotypes from distant lineages, or even new genes bearing new functions, it is a major source of phenotypic innovation and a mechanism of niche adaptation. For example, of particular relevance to human health is the lateral transfer of antibiotic resistance and pathogenicity determinants, leading to the emergence of pathogenic lineages. Computational identification of HGT events relies upon the investigation of sequence composition or evolutionary history of genes. Sequence composition-based ("parametric" methods search for deviations from the genomic average, whereas evolutionary history-based ("phylogenetic" approaches identify genes whose evolutionary history significantly differs from that of the host species. The evaluation and benchmarking of HGT inference methods typically rely upon simulated genomes, for which the true history is known. On real data, different methods tend to infer different HGT events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events.
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.
Risk and causality in newspaper reporting.
Boholm, Max
2009-11-01
The study addresses the textual representation of risk and causality in news media reporting. The analytical framework combines two theoretical perspectives: media frame analysis and the philosophy of causality. Empirical data derive from selected newspaper articles on risks in the Göta älv river valley in southwest Sweden from 1994 to 2007. News media content was coded and analyzed with respect to causal explanations of risk issues. At the level of individual articles, this study finds that the media provide simple causal explanations of risks such as water pollution, landslides, and flooding. Furthermore, these explanations are constructed, or framed, in various ways, the same risk being attributed to different causes in different articles. However, the study demonstrates that a fairly complex picture of risks in the media emerges when extensive material is analyzed systematically.
Rate-Agnostic (Causal) Structure Learning.
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.
Perception of Body Ownership Is Driven by Bayesian Sensory Inference
Samad, Majed; Chung, Albert Jin; Shams, Ladan
2015-01-01
Recent studies have shown that human perception of body ownership is highly malleable. A well-known example is the rubber hand illusion (RHI) wherein ownership over a dummy hand is experienced, and is generally believed to require synchronized stroking of real and dummy hands. Our goal was to elucidate the computational principles governing this phenomenon. We adopted the Bayesian causal inference model of multisensory perception and applied it to visual, proprioceptive, and tactile stimuli. The model reproduced the RHI, predicted that it can occur without tactile stimulation, and that synchronous stroking would enhance it. Various measures of ownership across two experiments confirmed the predictions: a large percentage of individuals experienced the illusion in the absence of any tactile stimulation, and synchronous stroking strengthened the illusion. Altogether, these findings suggest that perception of body ownership is governed by Bayesian causal inference—i.e., the same rule that appears to govern the perception of outside world. PMID:25658822
Causales de ausencia de responsabilidad penal
Directory of Open Access Journals (Sweden)
Jaime Sandoval Fernández
2003-01-01
Full Text Available Este trabajo se ocupa de las causales de ausencia de responsabilidad penal, especialmente de aquellas que tienen efecto en el injusto. Como subtemas se delimita el concepto de responsabilidad penal y su ausencia. Se estudian las principales teorias a cerca de la relación tipicidad-antijuridicidad y su incidencia en el derecho penal colombiano. Por último contiene una propuesta acerca de cómo deberian agruparse las causales del arto 32 C. PlOO.
Sex differences in the inference and perception of causal relations within a video game
Young, Michael E.
2014-01-01
The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy ...
A framework for inferring predictive distributions of rhino poaching events through causal modelling
CSIR Research Space (South Africa)
Koen, H
2014-07-01
Full Text Available activities usually cannot be directly observed, but we can observe phenomena that are related to poaching in different ways. Firstly, some observations provide clues about phenomena that facilitate poaching, such as certain weather conditions, the moon phase.... In such a case, the input would be soft evidence. Ranger present, a binary variable that takes on value true if a ranger is present at location Ai. Season, a discrete variable with four states corresponding to the four seasons. Moon, a discrete variable...
Linden, Ariel
2017-08-01
When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators. Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model. Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified. Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class. © 2017 John Wiley & Sons, Ltd.
Explaining Racial Disparities in Child Asthma Readmission Using a Causal Inference Approach.
Beck, Andrew F; Huang, Bin; Auger, Katherine A; Ryan, Patrick H; Chen, Chen; Kahn, Robert S
2016-07-01
Childhood asthma is characterized by disparities in the experience of morbidity, including the risk for readmission to the hospital after an initial hospitalization. African American children have been shown to have more than 2 times the hazard of readmission when compared with their white counterparts. To explain why African American children are at greater risk for asthma-related readmissions than white children. This study was completed as part of the Greater Cincinnati Asthma Risks Study, a population-based, prospective, observational cohort. From August 2010 to October 2011, it enrolled 695 children, aged 1 to 16 years, admitted for asthma or wheezing who identified as African American (n = 441) or white (n = 254) in an inpatient setting of an urban, tertiary care children's hospital. The main outcome was time to asthma-related readmission and race was the predictor. Biologic, environmental, disease management, access, and socioeconomic hardship variables were measured; their roles in understanding racial readmission disparities were conceptualized using a directed acyclic graphic. Inverse probability of treatment weighting balanced African American and white children with respect to key measured variables. Racial differences in readmission hazard were assessed using weighted Cox proportional hazards regression and Kaplan-Meier curves. The sample was 65% male (n = 450), and the median age was 5.4 years. African American children were 2.26 times more likely to be readmitted than white children (95% CI, 1.56-3.26). African American children significantly differed with respect to nearly every measured biologic, environmental, disease management, access, and socioeconomic hardship variable. Socioeconomic hardship variables explained 53% of the observed disparity (hazard ratio, 1.47; 95% CI, 1.05-2.05). The addition of biologic, environmental, disease management, and access variables resulted in 80% of the readmission disparity being explained. The 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.
Lewis, Sarah J.; Relton, Caroline; Zammit, Stanley; Smith, George Davey
2013-01-01
Background: The risk of childhood behavioural and psychiatric diseases could be substantially reduced if modifiable risk factors for these disorders were identified. The critical period for many of these exposures is likely to be in utero as this is the time when brain development is most rapid. However, due to confounding and other limitations of…
A meta-frontier approach for causal inference in productivity analysis
DEFF Research Database (Denmark)
Henningsen, Arne; Mpeta, Daniel F.; Adem, Anwar S.
(2012) and create a meta-frontier in order to estimate the effects of participation on the farms’ meta-technology ratio, their group technical efficiency, and their meta-technology technical efficiency. The empirical analysis uses a cross-sectional data set from sunflower farmers in Tanzania, where some...
Causal inference and temporal predictions in audiovisual perception of speech and music.
Noppeney, Uta; Lee, Hwee Ling
2018-03-31
To form a coherent percept of the environment, the brain must integrate sensory signals emanating from a common source but segregate those from different sources. Temporal regularities are prominent cues for multisensory integration, particularly for speech and music perception. In line with models of predictive coding, we suggest that the brain adapts an internal model to the statistical regularities in its environment. This internal model enables cross-sensory and sensorimotor temporal predictions as a mechanism to arbitrate between integration and segregation of signals from different senses. © 2018 New York Academy of Sciences.
Causal associations between risk factors and common diseases inferred from GWAS summary data
DEFF Research Database (Denmark)
Zhu, Zhihong; Zheng, Zhili; Zhang, Futao
2018-01-01
Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called...
Causal Inference from Big Data: Theoretical Foundations and the Data-fusion Problem
2015-06-01
represents informa- tion obtainable from non-experimental studies on the current population, where S = s. The problems represented in these archetypal ...conditions are likely to be different. Special cases of transportability can be found in the literature under different rubrics such as “external validity
Editorial: Ingenious designs and causal inference in child psychology and psychiatry.
Green, Jonathan
2016-05-01
The embryology of behavior--This title of a book by the great developmental psychologist Arnold Gesell (Gesell, 1945) continues nicely to encapsulate for me a core endeavour in child psychology and psychiatry; in the use of scientific method to tease out causes and processes within developmental science and psychopathology. This edition of JCPP includes some tremendous examples of the increasing rigour and sophistication with which such questions are being addressed. Particularly encouraging for me, as primarily an interventionist, is the use of well-designed randomized controlled trials (RCTs) for that end. © 2016 Association for Child and Adolescent Mental Health.
Directory of Open Access Journals (Sweden)
Xudong Dai
2007-03-01
Full Text Available Uncovering pathways underlying drug-induced toxicity is a fundamental objective in the field of toxicogenomics. Developing mechanism-based toxicity biomarkers requires the identification of such novel pathways and the order of their sufficiency in causing a phenotypic response. Genome-wide RNA interference (RNAi phenotypic screening has emerged as an effective tool in unveiling the genes essential for specific cellular functions and biological activities. However, eliciting the relative contribution of and sufficiency relationships among the genes identified remains challenging. In the rodent, the most widely used animal model in preclinical studies, it is unrealistic to exhaustively examine all potential interactions by RNAi screening. Application of existing computational approaches to infer regulatory networks with biological outcomes in the rodent is limited by the requirements for a large number of targeted permutations. Therefore, we developed a two-step relay method that requires only one targeted perturbation for genome-wide de novo pathway discovery. Using expression profiles in response to small interfering RNAs (siRNAs against the gene for peroxisome proliferator-activated receptor alpha (Ppara, our method unveiled the potential causal sufficiency order network for liver hypertrophy in the rodent. The validity of the inferred 16 causal transcripts or 15 known genes for PPARalpha-induced liver hypertrophy is supported by their ability to predict non-PPARalpha-induced liver hypertrophy with 84% sensitivity and 76% specificity. Simulation shows that the probability of achieving such predictive accuracy without the inferred causal relationship is exceedingly small (p < 0.005. Five of the most sufficient causal genes have been previously disrupted in mouse models; the resulting phenotypic changes in the liver support the inferred causal roles in liver hypertrophy. Our results demonstrate the feasibility of defining pathways mediating drug
Statistical inferences in phylogeography
DEFF Research Database (Denmark)
Nielsen, Rasmus; Beaumont, Mark A
2009-01-01
can randomly lead to multiple different genealogies. Likewise, the same gene trees can arise under different demographic models. This problem has led to the emergence of many statistical methods for making phylogeographic inferences. A popular phylogeographic approach based on nested clade analysis...... is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user, and it has been argued that the method performs poorly in simulation studies. More rigorous statistical methods based on coalescence theory have been developed. However, these methods...... may also be challenged by computational problems or poor model choice. In this review, we will describe the development of statistical methods in phylogeographic analysis, and discuss some of the challenges facing these methods....
Illness causal beliefs in Turkish immigrants.
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
Admissibility of logical inference rules
Rybakov, VV
1997-01-01
The aim of this book is to present the fundamental theoretical results concerning inference rules in deductive formal systems. Primary attention is focused on: admissible or permissible inference rules the derivability of the admissible inference rules the structural completeness of logics the bases for admissible and valid inference rules. There is particular emphasis on propositional non-standard logics (primary, superintuitionistic and modal logics) but general logical consequence relations and classical first-order theories are also considered. The book is basically self-contained and
Entanglement entropy in causal set theory
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.
Preschoolers prefer to learn causal information
Directory of Open Access Journals (Sweden)
Aubry eAlvarez
2015-02-01
Full Text Available Young children, in general, appear to have a strong drive to explore the environment in ways that reveal its underlying causal structure. But are they really attuned specifically to casual information in this quest for understanding, or do they show equal interest in other types of non-obvious information about the world? To answer this question, we introduced 20 three-year-old children to two puppets who were anxious to tell the child about a set of novel artifacts and animals. One puppet consistently described causal properties of the items while the other puppet consistently described carefully matched non-causal properties of the same items. After a familiarization period in which children learned which type of information to expect from each informant, children were given the opportunity to choose which they wanted to hear describe each of eight pictured test items. On average, children chose to hear from the informant that provided causal descriptions on 72% of the trials. This preference for causal information has important implications for explaining the role of conceptual information in supporting early learning and may suggest means for maximizing interest and motivation in young children.
Psychiatric comorbidity and causal disease models.
van Loo, Hanna M; Romeijn, Jan-Willem; de Jonge, Peter; Schoevers, Robert A
2013-12-01
In psychiatry, comorbidity is the rule rather than the exception. Up to 45% of all patients are classified as having more than one psychiatric disorder. These high rates of comorbidity have led to a debate concerning the interpretation of this phenomenon. Some authors emphasize the problematic character of the high rates of comorbidity because they indicate absent zones of rarities. Others consider comorbid conditions to be a validator for a particular reclassification of diseases. In this paper we will show that those at first sight contrasting interpretations of comorbidity are based on similar assumptions about disease models. The underlying ideas are that firstly high rates of comorbidity are the result of the absence of causally defined diseases in psychiatry, and second that causal disease models are preferable to non-causal disease models. We will argue that there are good reasons to seek after causal understanding of psychiatric disorders, but that causal disease models will not rule out high rates of comorbidity--neither in psychiatry, nor in medicine in general. By bringing to the fore these underlying assumptions, we hope to clear the ground for a different understanding of comorbidity, and of models for psychiatric diseases. Copyright © 2012 Elsevier Inc. All rights reserved.
The Relevance of Causal Social Construction
Directory of Open Access Journals (Sweden)
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.
Sensorimotor Network Crucial for Inferring Amusement from Smiles.
Paracampo, Riccardo; Tidoni, Emmanuele; Borgomaneri, Sara; di Pellegrino, Giuseppe; Avenanti, Alessio
2017-11-01
Understanding whether another's smile reflects authentic amusement is a key challenge in social life, yet, the neural bases of this ability have been largely unexplored. Here, we combined transcranial magnetic stimulation (TMS) with a novel empathic accuracy (EA) task to test whether sensorimotor and mentalizing networks are critical for understanding another's amusement. Participants were presented with dynamic displays of smiles and explicitly requested to infer whether the smiling individual was feeling authentic amusement or not. TMS over sensorimotor regions representing the face (i.e., in the inferior frontal gyrus (IFG) and ventral primary somatosensory cortex (SI)), disrupted the ability to infer amusement authenticity from observed smiles. The same stimulation did not affect performance on a nonsocial task requiring participants to track the smiling expression but not to infer amusement. Neither TMS over prefrontal and temporo-parietal areas supporting mentalizing, nor peripheral control stimulations, affected performance on either task. Thus, motor and somatosensory circuits for controlling and sensing facial movements are causally essential for inferring amusement from another's smile. These findings highlight the functional relevance of IFG and SI to amusement understanding and suggest that EA abilities may be grounded in sensorimotor networks for moving and feeling the body. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)
2017-05-25
Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.
Multistability and perceptual inference.
Gershman, Samuel J; Vul, Edward; Tenenbaum, Joshua B
2012-01-01
Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent uncertainty in the form of a posterior probability distribution over causes. However, in many real-world situations, computing this distribution is intractable and requires some form of approximation. We argue that the visual system approximates the posterior over underlying causes with a set of samples and that this approximation strategy produces perceptual multistability--stochastic alternation between percepts in consciousness. Under our analysis, multistability arises from a dynamic sample-generating process that explores the posterior through stochastic diffusion, implementing a rational form of approximate Bayesian inference known as Markov chain Monte Carlo (MCMC). We examine in detail the most extensively studied form of multistability, binocular rivalry, showing how a variety of experimental phenomena--gamma-like stochastic switching, patchy percepts, fusion, and traveling waves--can be understood in terms of MCMC sampling over simple graphical models of the underlying perceptual tasks. We conjecture that the stochastic nature of spiking neurons may lend itself to implementing sample-based posterior approximations in the brain.
Normalizing the causality between time series
Liang, X. San
2015-08-01
Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) 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 mainframe computer market.
Causal inheritance in plane wave quotients
International Nuclear Information System (INIS)
Hubeny, Veronika E.; Rangamani, Mukund; Ross, Simon F.
2003-01-01
We investigate the appearance of closed timelike curves in quotients of plane waves along spacelike isometries. First we formulate a necessary and sufficient condition for a quotient of a general spacetime to preserve stable causality. We explicitly show that the plane waves are stably causal; in passing, we observe that some pp-waves are not even distinguishing. We then consider the classification of all quotients of the maximally supersymmetric ten-dimensional plane wave under a spacelike isometry, and show that the quotient will lead to closed timelike curves iff the isometry involves a translation along the u direction. The appearance of these closed timelike curves is thus connected to the special properties of the light cones in plane wave spacetimes. We show that all other quotients preserve stable causality
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
Zenil, Hector
2018-02-18
To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.
mpdcm: A toolbox for massively parallel dynamic causal modeling.
Aponte, Eduardo A; Raman, Sudhir; Sengupta, Biswa; Penny, Will D; Stephan, Klaas E; Heinzle, Jakob
2016-01-15
Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs). Simulations are generated in parallel by implementing a low storage explicit Runge-Kutta's scheme on a GPU architecture. mpdcm is publicly available under the GPLv3 license. We found that mpdcm efficiently generates large number of simulations without compromising their accuracy. As applications of mpdcm, we suggest two computationally expensive sampling algorithms: thermodynamic integration and parallel tempering. mpdcm is up to two orders of magnitude more efficient than the standard implementation in the software package SPM. Parallel tempering increases the mixing properties of the traditional Metropolis-Hastings algorithm at low computational cost given efficient, parallel simulations of a model. Future applications of DCM will likely require increasingly large computational resources, for example, when the likelihood landscape of a model is multimodal, or when implementing sampling methods for multi-subject analysis. Due to the wide availability of GPUs, algorithmic advances can be readily available in the absence of access to large computer grids, or when there is a lack of expertise to implement algorithms in such grids. Copyright © 2015 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Dergiades, Theologos; Martinopoulos, Georgios; Tsoulfidis, Lefteris
2013-01-01
The objective of this paper is to contribute towards the understanding of the linear and non-linear causal linkages between energy consumption and economic activity, making use of annual time series data of Greece for the period 1960–2008. Two are the salient features of our study: first, the total energy consumption has been adjusted for qualitative differences among its constituent components through the thermodynamics of energy conversion. In doing so, we rule out the possibility of a misleading inference with respect to causality due to aggregation bias. Second, the investigation of the causal linkage between economic growth and the adjusted for quality total energy consumption is conducted within a non-linear context. Our empirical results reveal significant unidirectional both linear and non-linear causal linkages running from total useful energy to economic growth. These findings may provide valuable information for the contemplation of more effective energy policies with respect to both the consumption of energy and environmental protection. - Highlights: ► The energy consumption and economic growth nexus is investigated for Greece. ► A quality-adjusted energy series is used in our analysis. ► The causality testing procedure is conducted within a non-linear context. ► A causality running from energy consumption to economic growth is verified
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.
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.
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory
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 relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739
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.
Causal analysis of self-sustaining processes in the logarithmic layer of wall-bounded turbulence
Bae, H. J.; Encinar, M. P.; Lozano-Durán, A.
2018-04-01
Despite the large amount of information provided by direct numerical simulations of turbulent flows, their underlying dynamics remain elusive even in the most simple and canonical configurations. Most common approaches to investigate the turbulence phenomena do not provide a clear causal inference between events, which is essential to determine the dynamics of self-sustaining processes. In the present work, we examine the causal interactions between streaks, rolls and mean shear in the logarithmic layer of a minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. We choose to represent streaks by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocity modes. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear which controls the dynamics and time-scales. The well-known lift-up effect is also identified, but shown to be of secondary importance in the causal network between shear, streaks and rolls.
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.
Gopnik, Alison; Wellman, Henry M
2012-11-01
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 nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
Morse theory on timelike and causal curves
International Nuclear Information System (INIS)
Everson, J.; Talbot, C.J.
1976-01-01
It is shown that the set of timelike curves in a globally hyperbolic space-time manifold can be given the structure of a Hilbert manifold under a suitable definition of 'timelike.' The causal curves are the topological closure of this manifold. The Lorentzian energy (corresponding to Milnor's energy, except that the Lorentzian inner product is used) is shown to be a Morse function for the space of causal curves. A fixed end point index theorem is obtained in which a lower bound for the index of the Hessian of the Lorentzian energy is given in terms of the sum of the orders of the conjugate points between the end points. (author)
Causal interpretation of stochastic differential equations
DEFF Research Database (Denmark)
Sokol, Alexander; Hansen, Niels Richard
2014-01-01
We give a causal interpretation of stochastic differential equations (SDEs) by defining the postintervention SDE resulting from an intervention in an SDE. We show that under Lipschitz conditions, the solution to the postintervention SDE is equal to a uniform limit in probability of postintervention...... structural equation models based on the Euler scheme of the original SDE, thus relating our definition to mainstream causal concepts. We prove that when the driving noise in the SDE is a Lévy process, the postintervention distribution is identifiable from the generator of the SDE....
Blocking in children's causal learning depends on working memory and reasoning abilities.
McCormack, Teresa; Simms, Victoria; McGourty, Jemma; Beckers, Tom
2013-07-01
A sample of 99 children completed a causal learning task that was an analogue of the food allergy paradigm used with adults. The cue competition effects of blocking and unovershadowing were assessed under forward and backward presentation conditions. Children also answered questions probing their ability to make the inference posited to be necessary for blocking by a reasoning account of cue competition. For the first time, children's working memory and general verbal ability were also measured alongside their causal learning. The magnitude of blocking and unovershadowing effects increased with age. However, analyses showed that the best predictor of both blocking and unovershadowing effects was children's performance on the reasoning questions. The magnitude of the blocking effect was also predicted by children's working memory abilities. These findings provide new evidence that cue competition effects such as blocking are underpinned by effortful reasoning processes. Copyright © 2013 Elsevier Inc. All rights reserved.
CausalTrail: Testing hypothesis using causal Bayesian networks [version 1; referees: 2 approved
Directory of Open Access Journals (Sweden)
Daniel Stöckel
2015-12-01
Full Text Available Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail
Groben, Sylvie; Hausteiner, Constanze
2011-03-01
Somatic causal illness attributions are being considered as potential positive criteria for somatoform disorders (SFDs) in DSM-V. The aim of this study was to investigate whether patients diagnosed with SFDs tend towards a predominantly somatic attribution style. We compared the causal illness attributions of 48 SFD and 149 non-somatoform disorder patients, in a sample of patients presenting for an allergy diagnostic work-up, and those of 47 controls hospitalised for allergen-specific venom immunotherapy. The SFD diagnosis was established by means of the Structured Clinical Interview for DSM-IV. Both spontaneous and prompted causal illness attributions were recorded through interview and by means of the causal dimension of the Revised Illness Perception Questionnaire (IPQ-R), respectively. Patients' spontaneous and prompted responses were assigned to a psychosocial, somatic, or mixed attribution style. Both in the free-response task and in their responses to the IPQ-R, SFD patients were no more likely than their nonsomatoform counterparts to focus on somatic explanations for their symptoms. They were just as likely to make psychosocial or mixed causal attributions. However, patients with SFDs were significantly more likely to find fault with medical care in the past. Our data do not support the use of somatic causal illness attributions as positive criteria for SFDs. They confirm the dynamic and multidimensional nature of causal illness attributions. Clinical implications of these findings are discussed. Copyright © 2011 Elsevier Inc. All rights reserved.
Boucher-Lalonde, Véronique; Currie, David J
2016-01-01
Species' geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species' ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species' climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species' ranges, are correlations between species' range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species' realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated.
Boucher-Lalonde, Véronique; Currie, David J.
2016-01-01
Species’ geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species’ ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species’ climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species’ ranges, are correlations between species’ range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species’ realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated. PMID:27855201
Information-Theoretic Inference of Common Ancestors
Directory of Open Access Journals (Sweden)
Bastian Steudel
2015-04-01
Full Text Available A directed acyclic graph (DAG partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.
How people explain their own and others’ behavior: A theory of lay causal explanations
Directory of Open Access Journals (Sweden)
Gisela eBöhm
2015-02-01
Full Text Available A theoretical model is proposed that speci¬fies lay causal theo¬ries of behavior; and supporting experimental evidence is presented. The model’s basic assumption is that diffe¬rent types of behavior trigger different hypotheses concerning the types of causes that may have brought about the behavior. Se¬ven categories are distinguished that are assumed to serve as both behavior types and explanation types: goals, disposi¬tions, tem¬po¬rary states such as emotions, intentional actions, outcomes, events, and sti¬mulus attributes. The mo¬del specifies inference rules that lay people use when explai¬ning beha¬vior (actions are caused by goals; goals are caused by higher order goals or temporary states; temporary states are caused by dispositions, stimulus attributes, or events; outcomes are caused by actions, temporary states, dispositions, stimulus attributes, or events; events are caused by dispositions or preceding events. Two experiments are reported. Experi¬ment 1 showed that free-response explanations followed the assumed inference rules. Expe¬ri¬ment 2 demonstrated that ex¬plana¬tions which match the inference rules are generated faster and more frequently than non-matching explanations. Together, the findings support models that incorporate knowledge-based aspects into the process of causal explanation. The results are discussed with respect to their implications for different stages of this process, such as the activation of causal hypotheses and their subsequent selection, as well as with respect to social influences on this process.
An Inference Language for Imaging
DEFF Research Database (Denmark)
Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen
2014-01-01
We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framework...... is composed of a set of language primitives and of an inference engine based on a message-passing system that integrates cutting-edge computational tools, including proximal algorithms and high performance Hamiltonian Markov Chain Monte Carlo techniques. A set of domain-specific highly optimized GPU...
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
Localization and causality in relativistic quantum mechanics
International Nuclear Information System (INIS)
Perez, J.F.; Wilde, I.F.
It is shown that in relativistic quantum mechanics there is no criterion for the strict localization of a state in a bounded space-time region compatible with causality, translation covariance and the spectral condition (or positivity of energy together with Lorentz covariance) [pt
Catastrophizing and Causal Beliefs in Whiplash
Buitenhuis, J.; de Jong, P. J.; Jaspers, J. P. C.; Groothoff, J. W.
2008-01-01
Study Design. Prospective cohort study. Objective. This study investigates the role of pain catastrophizing and causal beliefs with regard to severity and persistence of neck complaints after motor vehicle accidents. Summary of Background Data. In previous research on low back pain, somatoform
Special Relativity, Causality and Quantum Mechanics - 1
Indian Academy of Sciences (India)
information theory in general and quantum non-locality and entanglement in particular. Right. S Kunkri - current research interest is the role of entanglement in quantum information processing and the connection between quantum operations and causality. Centre. S K Choudhary - current research interest is the study of ...
Marriage and Anomie: A Causal Argument
Lee, Gary R.
1974-01-01
A sample of 394 married couples is employed to test the possibility of an association between marital satisfaction and personal (attitudinal) anomie. The hypothesis is supported. Conclusions are offered relevant to anomie theory, and to utilization of marital and family phenomena as independent variables in causal explanations of nonfamily events.…
Causal Measurement Models: Can Criticism Stimulate Clarification?
Markus, Keith A.
2016-01-01
In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…
Causal Meta-Analysis : Methodology and Applications
Bax, L.J.
2009-01-01
Meta-analysis is a statistical method to summarize research data from multiple studies in a quantitative manner. This dissertation addresses a number of methodological topics in causal meta-analysis and reports the development and validation of meta-analysis software. In the first (methodological)
A Causal Model of Faculty Turnover Intentions.
Smart, John C.
1990-01-01
A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…
Black Hole Complementarity and Violation of Causality
Rozenblit, Moshe
2017-01-01
Analysis of a massive shell collapsing on a solid sphere shows that black hole complementarity (BHC) violates causality in its effort to save information conservation. In particular, this note describes a hypothetical contraption based on BHC that would allow the transfer of information from the future to the present.
THE CAUSAL TEXTURE OF TRADE UNION ENVIRONMENTS
African Journals Online (AJOL)
Admin
This paper is an attempt to fill an important gap in the existing literature on trade unions by providing a more adequate theoretical formulation of trade union environments. The discussion suggests that unlike the environment of business and related organisations whose causal texture is understood in terms of uncertainty, ...
Are bruxism and the bite causally related?
Lobbezoo, F.; Ahlberg, J.; Manfredini, D.; Winocur, E.
2012-01-01
In the dental profession, the belief that bruxism and dental (mal-)occlusion (‘the bite’) are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query ‘Bruxism [Majr
Sequential causal learning in humans and rats
Lu, H.; Rojas, R.R.; Beckers, T.; Yuille, A.; Love, B.C.; McRae, K.; Sloutsky, V.M.
2008-01-01
Recent experiments (Beckers, De Houwer, Pineño, & Miller, 2005;Beckers, Miller, De Houwer, & Urushihara, 2006) have shown that pretraining with unrelated cues can dramatically influence the performance of humans in a causal learning paradigm and rats in a standard Pavlovian conditioning paradigm.
Dimensional reduction in causal set gravity
Carlip, S.
2015-12-01
Results from a number of different approaches to quantum gravity suggest that the effective dimension of spacetime may drop to d = 2 at small scales. I show that two different dimensional estimators in causal set theory display the same behavior, and argue that a third, the spectral dimension, may exhibit a related phenomenon of ‘asymptotic silence.’
The Causal Relationship between Financial Development and ...
African Journals Online (AJOL)
The study employs cointegration, vector error correction model and Granger causality test to ascertain causation between financial development and economic performance in Tanzania. Economic performance is measured by the real GDP, whereas proxies for financial development are: the ratio of money supply to nominal ...
Causal and Teleological Explanations in Biology
Yip, Cheng-Wai
2009-01-01
A causal explanation in biology focuses on the mechanism by which a biological process is brought about, whereas a teleological explanation considers the end result, in the context of the survival of the organism, as a reason for certain biological processes or structures. There is a tendency among students to offer a teleological explanation…
Special Relativity, Causality and Quantum Mechanics - 2
Indian Academy of Sciences (India)
Peaceful Coexistence of Special Relativity and. Quantum Mechanics. As discussed in Part 1, in the framework of the special theory of relativity, causality holds. This can be stated as follows: there is a finite speed for any signal, i.e. , for anything that carries information, and the highest speed for any signal is identical to the ...
Causal Relationship between Teachers' Job Performance and ...
African Journals Online (AJOL)
The study investigated teachers' job performance and students' academic achievement in secondary schools for the existence of bi-causal relationship in Nigeria. The ex-post facto research design was adopted in the study. The population of the study covered all the Economic teachers and senior school students in class ...
Introducing mechanics by tapping core causal knowledge
Klaassen, C.W.J.M.; Westra, A.S.; Emmett, K.M.; Eijkelhof, H.M.C.; Lijnse, P.L.
2008-01-01
This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force
Causality and analyticity in quantum fields theory
International Nuclear Information System (INIS)
Iagolnitzer, D.
1992-01-01
This is a presentation of results on the causal and analytical structure of Green functions and on the collision amplitudes in fields theories, for massive particles of one type, with a positive mass and a zero spin value. (A.B.)
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 ...
The Causal Priority of Form in Aristotle
Directory of Open Access Journals (Sweden)
Kathrin Koslicki
2014-12-01
Full Text Available In various texts (e.g., Met. Z.17, Aristotle assigns priority to form, in its role as a principle and cause, over matter and the matter-form compound. Given the central role played by this claim in Aristotle's search for primary substance in the Metaphysics, it is important to understand what motivates him in locating the primary causal responsibility for a thing's being what it is with the form, rather than the matter. According to Met. Theta.8, actuality [energeia/entelecheia] in general is prior to potentiality [dunamis] in three ways, viz., in definition, time and substance. I propose an explicitly causal reading of this general priority claim, as it pertains to the matter-form relationship. The priority of form over matter in definition, time and substance, in my view, is best explained by appeal to the role of form as the formal, efficient and final cause of the matter-form compound, respectively, while the posteriority of matter to form according to all three notions of priority is most plausibly accounted for by the fact that the causal contribution of matter is limited to its role as material cause. When approached from this angle, the work of Met. Theta.8 can be seen to lend direct support to the more specific and explicitly causal priority claim we encounter in Met. Z.17, viz., that form is prior to matter in its role as the principle and primary cause of a matter-form compound's being what it is.
Special Relativity, Causality and Quantum Mechanics - 1
Indian Academy of Sciences (India)
We discuss the significance of Einstein's second postulate of the special theory of relativity (STR) stipulating the constancy of the speed of light in vacuum. The causality that follows from the. STR may be a more general principle to orga- nize our knowledge of all phenomena. In partic- ular, quantum dynamics can be derived ...
Special Relativity, Causality and Quantum Mechanics - 2
Indian Academy of Sciences (India)
tum world. An example of a game which can be won exploiting quantum entanglement, but which can never be won classically, is described. Peaceful Coexistence of Special Relativity and. Quantum Mechanics. As discussed in Part 1, in the framework of the special theory of relativity, causality holds. This can be stated.
Probable autoimmune causal relationship between periodontitis and ...
African Journals Online (AJOL)
Periodontitis is a multifactorial disease with microbial dental plaque as the initiator of periodontal disease. However, the manifestation and progression of the disease is influenced by a wide variety of determinants and factors. The strongest type of causal relationship is the association of systemic and periodontal disease.
Directory of Open Access Journals (Sweden)
Agota Major
2010-06-01
Full Text Available This study aims to investigate the effect of theory of mind, age and mother tongue on the implicit causality effect in preschoolers from two different language backgrounds. Serbian and Hungarian native speakers aged 3–7 years participated in the study. After taking part in a Theory of Mind task, children were presented verbs in simple „Subject verb Object” sentences describing interactions between two participants, with the interactions being based on emotional, mental or visual experiences. Children were asked “Why does S verb O?” and their responses were categorized as containing an inference about the sentence-S or the sentence-O. The results show that Theory of Mind is a significant factor in the emergence of implicit causality, with age of participants and mother tongue being also contributing to explaining patterns of implicit causality.
2017-01-01
Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence. PMID:28747331
Reed, Zoe E; Micali, Nadia; Bulik, Cynthia M; Davey Smith, George; Wade, Kaitlin H
2017-09-01
Background: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) ( n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence.
"Cutaneous rabbit" hops toward a light: Unimodal and cross-modal causality on the skin
Directory of Open Access Journals (Sweden)
Tomohisa eAsai
2012-10-01
Full Text Available Our somatosensory system deals with not only spatial but also temporal imprecision, resulting in characteristic spatiotemporal illusions. Repeated rapid stimulation at the wrist, then near the elbow, can create the illusion of touch at intervening locations along the arm (as if a rabbit is hopping along the arm. This is known as the cutaneous rabbit effect (CRE. Previous studies have suggested that the CRE involves not only an intrinsic somatotopic representation but also the representation of an extended body schema that includes causality or animacy perception upon the skin. On the other hand, unlike other multi-modal causality couplings, it is possible that the CRE is not affected by concurrent auditory temporal information. The present study examined the effect of a simple visual flash on the CRE, which has both temporal and spatial information. Here, stronger cross-modal causality or correspondence could be provided. We presented three successive tactile stimuli on the inside of a participant’s left arm. Stimuli were presented on the wrist, elbow, and midway between the two. Results from our five experimental manipulations suggest that a one-shot flash enhances or attenuates the CRE depending on its congruency with cutaneous rabbit saltation. Our results reflect that 1 our brain interprets successive stimuli on the skin as motion in terms of time and space (unimodal causality and that 2 the concurrent signals from other modalities provide clues for creating unified representations of this external motion (multi-modal causality as to the extent that spatiotemporal synchronicity among modalities is provided.available information from other modalities should also provide a key clue as to the extent that spatiotemporal synchronicity among modalities is provided.
Kushnir, Tamar; Vredenburgh, Christopher; Schneider, Lauren A
2013-03-01
Preschoolers use outcomes of actions to infer causal properties of objects. We asked whether they also use them to infer others' causal abilities and knowledge. In Experiment 1, preschoolers saw 2 informants, 2 tools, and 2 broken toys. One informant (the labeler) knew the names of the tools, but his actions failed to activate the toys. The other (the fixer) was ignorant about the names of the tools, but his actions succeeded in activating the toys. Four-year-olds (and to a lesser extent, 3-year-olds) selectively directed requests for new labels to the labeler and directed requests to fix new broken toys to the fixer. In a second experiment, 4-year-olds also endorsed a fixer's (over a nonfixer's) causal explanations for mechanical failures. They did not, however, ask the fixer about new words (Experiments 1 and 2) or artifact functions (Experiment 1). Thus, preschoolers take demonstrated causal ability as a sign of specialized causal knowledge, which suggests a basis for developing ideas about causal expertise.
Statistical inference via fiducial methods
Salomé, Diemer
1998-01-01
In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary
On principles of inductive inference
Kostecki, Ryszard Paweł
2011-01-01
We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.
Statistical inference for stochastic processes
National Research Council Canada - National Science Library
Basawa, Ishwar V; Prakasa Rao, B. L. S
1980-01-01
The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.
Is preschool executive function causally related to academic achievement?
Willoughby, Michael T; Kupersmidt, Janis B; Voegler-Lee, Mary E
2012-01-01
The primary objective of this study was to reevaluate the well-established result that preschoolers' performance on executive function tasks are positively associated with their performance on academic achievement tests. The current study replicated the previously established concurrent associations between children's performance on EF tasks and academic achievement tests. Specifically, children's performance on measures of inhibitory and motor control were positively associated with their performance on tests of reading, writing, and mathematics achievement (rs = .2-.5); moreover, although diminished in magnitude, most of these associations held up even after including an earlier measure of academic achievement as a covariate (rs = .1-.3). However, the application of an alternative analytic method, fixed effects analysis, a method that capitalizes on repeated measures data to control for all time stable measured and unmeasured covariates, rendered the apparent positive associations between executive function and academic achievement nonsignificant (rs = .0-.1). Taken together, these results suggest that the well-replicated association between executive function abilities and academic achievement may be spurious. Results are discussed with respect to the importance of utilizing analytic methods and research designs that facilitate strong causal inferences between executive function and academic achievement in early childhood, as well as the limitations of making curriculum development recommendations and/or public policy decisions based on studies that have failed to do so.
Gradient-based MCMC samplers for dynamic causal modelling.
Sengupta, Biswa; Friston, Karl J; Penny, Will D
2016-01-15
In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated using Hamilton's equation of motion and (b) Langevin Monte Carlo algorithm (LMC-R and LMC-E) that simulates the Langevin diffusion of samples using gradients either on a Euclidean (E) or on a Riemannian (R) manifold. While LMC-R requires minimal tuning, the implementation of HMC-E is heavily dependent on its tuning parameters. These parameters are therefore optimised by learning a Gaussian process model of the time-normalised sample correlation matrix. This allows one to formulate an objective function that balances tuning parameter exploration and exploitation, furnishing an intervention-free inference scheme. Using neural mass models (NMMs)-a class of biophysically motivated DCMs-we find that HMC-E is statistically more efficient than LMC-R (with a Riemannian metric); yet both gradient-based samplers are far superior to the random walk Metropolis algorithm, which proves inadequate to steer away from dynamical instability. Copyright © 2015. Published by Elsevier Inc.
Directory of Open Access Journals (Sweden)
Avery E. Scherer
2017-02-01
Full Text Available By influencing critical prey traits such as foraging or habitat selection, predators can affect entire ecosystems, but the nature of cues that trigger prey reactions to predators are not well understood. Predators may scavenge to supplement their energetic needs and scavenging frequency may vary among individuals within a species due to preferences and prey availability. Yet prey reactions to consumers that are primarily scavengers versus those that are active foragers have not been investigated, even though variation in prey reactions to scavengers or predators might influence cascading nonconsumptive effects in food webs. Oysters Crassostrea virginica react to crab predators by growing stronger shells. We exposed oysters to exudates from crabs fed live oysters or fed aged oyster tissue to simulate scavenging, and to controls without crab cues. Oysters grew stronger shells when exposed to either crab exudate, but their shells were significantly stronger when crabs were fed live oysters. The stronger response to predators than scavengers could be due to inherent differences in diet cues representative of reduced risk in the presence of scavengers or to degradation of conspecific alarm cues in aged treatments, which may mask risk from potential predators subsisting by scavenging.
Stronger Accent Following a Stroke: The Case of a Trilingual with Aphasia
Levy, Erika S.; Goral, Mira; De Diesbach, Catharine Castelluccio; Law, Franzo, II
2011-01-01
This study documents patterns of change in speech production in a multilingual with aphasia following a cerebrovascular accident (CVA). EC, a right-handed Hebrew-English-French trilingual man, had a left fronto-temporo-parietal CVA, after which he reported that his (native) Hebrew accent became stronger in his (second language) English. Recordings…
Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity
DEFF Research Database (Denmark)
Harndahl, Mikkel Nors; Rasmussen, Michael; Nielsen, Morten
2012-01-01
Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity Mikkel Harndahla, Michael Rasmussena, Morten Nielsenb, Soren Buusa,∗ a Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen, Denmark b Center for Biological Seq...... al., 2007. J. Immunol. 178, 7890–7901. doi:10.1016/j.molimm.2012.02.025...
First-order dominance: stronger characterization and a bivariate checking algorithm
DEFF Research Database (Denmark)
Range, Troels Martin; Østerdal, Lars Peter Raahave
2018-01-01
distributions. Utilizing that this problem can be formulated as a transportation problem with a special structure, we provide a stronger characterization of multivariate first-order dominance and develop a linear time complexity checking algorithm for the bivariate case. We illustrate the use of the checking...
Fasting insulin is a stronger cardiovascular risk factor in women than in men
Oterdoom, Leendert H.; de Vries, Aiko P. J.; Gansevoort, Ron T.; de Jong, Paul E.; Gans, Reinold O. B.; Bakker, Stephan J. L.
Diabetes is a stronger risk factor for cardiovascular disease (CVD) in women than in men. It is not known whether there is also a sex difference in the association between hyperinsulinaemia, reflecting insulin resistance, and CVD. Fasting insulin was assessed with a specific assay in 6916 fasting,
DEFF Research Database (Denmark)
Heydorn, S; Andersen, Klaus Ejner; Johansen, Jeanne Duus
2003-01-01
Household and cleaning products often contain both allergens and irritants. The aim of this double-blinded, randomized, paired study was to determine whether patch testing with an allergen (hydroxycitronellal) combined with an irritant [sodium lauryl sulfate (SLS)] cause a stronger patch test...
Myung, Jeannie; Martinez, Krissia; Nordstrum, Lee
2013-01-01
Building a stronger teacher workforce requires the thoughtful orchestration of multiple processes working together in a human capital system. This white paper presents a framework that can be used to take stock of current efforts to enhance the teacher workforce in school districts or educational organizations, as well as their underlying theories…
Harmful drinking after job loss: a stronger association during the post-2008 economic crisis?
de Goeij, Moniek C. M.; Bruggink, Jan-Willem; Otten, Ferdy; Kunst, Anton E.
2017-01-01
This study investigated, among the Dutch working population, whether job loss during the post-2008 economic crisis is associated with harmful drinking and whether this association is stronger than before the crisis. Repeated cross-sectional data from the Dutch Health Interview Survey 2004-2013 were
Brion, Marie-Jo A; Lawlor, Debbie A; Matijasevich, Alicia; Horta, Bernardo; Anselmi, Luciana; Araújo, Cora L; Menezes, Ana Maria B; Victora, Cesar G; Smith, George Davey
2011-01-01
Background A novel approach is explored for improving causal inference in observational studies by comparing cohorts from high-income with low- or middle-income countries (LMIC), where confounding structures differ. This is applied to assessing causal effects of breastfeeding on child blood pressure (BP), body mass index (BMI) and intelligence quotient (IQ). Methods Standardized approaches for assessing the confounding structure of breastfeeding by socio-economic position were applied to the British Avon Longitudinal Study of Parents and Children (ALSPAC) (N ≃ 5000) and Brazilian Pelotas 1993 cohorts (N ≃ 1000). This was used to improve causal inference regarding associations of breastfeeding with child BP, BMI and IQ. Analyses were extended to include results from a meta-analysis of five LMICs (N ≃ 10 000) and compared with a randomized trial of breastfeeding promotion. Findings Although higher socio-economic position was strongly associated with breastfeeding in ALSPAC, there was little such patterning in Pelotas. In ALSPAC, breastfeeding was associated with lower BP, lower BMI and higher IQ, adjusted for confounders, but in the directions expected if due to socioeconomic patterning. In contrast, in Pelotas, breastfeeding was not strongly associated with BP or BMI but was associated with higher IQ. Differences in associations observed between ALSPAC and the LMIC meta-analysis were in line with those observed between ALSPAC and Pelotas, but with robust evidence of heterogeneity detected between ALSPAC and the LMIC meta-analysis associations. Trial data supported the conclusions inferred by the cross-cohort comparisons, which provided evidence for causal effects on IQ but not for BP or BMI. Conclusion While reported associations of breastfeeding with child BP and BMI are likely to reflect residual confounding, breastfeeding may have causal effects on IQ. Comparing associations between populations with differing confounding structures can be used
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
Active inference, communication and hermeneutics☆
Friston, Karl J.; Frith, Christopher D.
2015-01-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007
Human brain lesion-deficit inference remapped.
Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev
2014-09-01
Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant
Keogh, Justin W L; Hume, Patria A; Pearson, Simon N; Mellow, Peter J
2009-11-01
This study sought to compare the anthropometric profiles of 17 weaker and 17 stronger Australasian and Pacific powerlifters who had competed in a regional-, national-, or international-level powerlifting competition in New Zealand. Stronger lifters were defined as those having a Wilks score greater than 410, whereas those in the weaker group had a Wilks score less than 370. Each powerlifter was assessed for 37 anthropometric dimensions by International Society for the Advancement of Kinanthropometry (ISAK) level II and III accredited anthropometrists. Because all powerlifters were highly mesomorphic and possessed large girths and bone breadths, both in absolute terms and when expressed as Phantom-Z scores compared through the Phantom, relatively few significant anthropometric differences were observed. However, stronger lifters had significantly greater muscle mass and larger muscular girths in absolute terms as well as greater Brugsch Index (chest girth/height) and "Phantom"-normalized muscle mass, upper arm, chest, and forearm girths. In terms of the segment lengths and bone breadths, the only significant difference was that stronger lifters had a significantly shorter lower leg than weaker lifters. Because the majority of the significant differences were for muscle mass and muscular girths, it would appear likely that these differences contributed to the stronger lifters' superior performance. Powerlifters may therefore need to devote some of their training to the development of greater levels of muscular hypertrophy if they wish to continue to improve their performance. To better understand the anthropometric determinants of muscular strength, future research should recruit larger samples (particularly of elite lifters) and follow these subjects prospectively.
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
Causal knowledge and the development of inductive reasoning.
Bright, Aimée K; Feeney, Aidan
2014-06-01
We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.
Violation of causality in f( T) gravity
Otalora, G.; Rebouças, M. J.
2017-11-01
In the standard formulation, the f( T) field equations are not invariant under local Lorentz transformations, and thus the theory does not inherit the causal structure of special relativity. Actually, even locally violation of causality can occur in this formulation of f( T) gravity. A locally Lorentz covariant f( T) gravity theory has been devised recently, and this local causality problem seems to have been overcome. The non-locality question, however, is left open. If gravitation is to be described by this covariant f( T) gravity theory there are a number of issues that ought to be examined in its context, including the question as to whether its field equations allow homogeneous Gödel-type solutions, which necessarily leads to violation of causality on non-local scale. Here, to look into the potentialities and difficulties of the covariant f( T) theories, we examine whether they admit Gödel-type solutions. We take a combination of a perfect fluid with electromagnetic plus a scalar field as source, and determine a general Gödel-type solution, which contains special solutions in which the essential parameter of Gödel-type geometries, m^2, defines any class of homogeneous Gödel-type geometries. We show that solutions of the trigonometric and linear classes (m^2 electromagnetic field matter component. We extended to the context of covariant f( T) gravity a theorem which ensures that any perfect-fluid homogeneous Gödel-type solution defines the same set of Gödel tetrads h_A^{ μ } up to a Lorentz transformation. We also showed that the single massless scalar field generates Gödel-type solution with no closed time-like curves. Even though the covariant f( T) gravity restores Lorentz covariance of the field equations and the local validity of the causality principle, the bare existence of the Gödel-type solutions makes apparent that the covariant formulation of f( T) gravity does not preclude non-local violation of causality in the form of closed time
Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)
Lozano, A. C.
2010-12-01
Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring
Inferring And Possibilities, Rather Than Natural Laws, In Robust Climate Modeling
Brumble, K. C.
2011-12-01
One concern raised about sciences which rely upon simulation models (such as climatology) is that the nature of simulations calls into question the soundness of the inferences that can be drawn from them. I argue that this concern stems from a belief that simulation models must provide laws in order to count as rigorous science, when in actual practice simulation models can investigate a variety of types of possibility with differing inferential potential, and that these differing inferences are necessary parts of the experimental process. I appeal to philosophical work in epistemology to make the argument that simulation models in general - and climate models in particular - explore different kinds of possibility, from logical possibilities to physical possibilities. I then argue that not only is this plurality of inference compatible with robust modeling practices, but that it leads to stronger inferences for climatology.
Violence in psychosis: conceptualizing its causal relationship with risk factors
Lamsma, J.; Harte, J.M.
2015-01-01
Background: While statistically robust, the association between psychosis and violence remains causally unexplained. Objective: To provide an overview of possible causal pathways between risk factors and violence in psychosis. Methods: A structured narrative review of relevant studies published
The Hankel transform of causal distributions
Directory of Open Access Journals (Sweden)
Manuel A. Aguirre T.
2012-03-01
Full Text Available In this note we evaluate the unidimensional distributional Hankel transform of \\dfrac{x^{\\alpha-1}_{+}}{\\Gamma^{\\alpha}},\\dfrac{x^{\\alpha-1}_{-}}{\\Gamma^{\\alpha}},dfrac{|x|^{\\alpha-1}}{\\Gamma^{\\frac{\\alpha}{2}}},dfrac{|x|^{\\alpha-1}sgn(x}{\\Gamma^{\\frac{\\alpha +1}{2}}} and (x± i0^{\\alpha-1} and then we extend the formulae to certain kinds of n-dimensional distributions calles "causal" and "anti-causal" distributions. We evaluate the distributional Handel transform of \\dfrac{(m^2+P^{\\alpha -1}_{-}}{\\Gamma^{(\\alpha} }, \\dfrac{|m^2+P|^{\\alpha -1}_{-}}{\\Gamma^{(\\frac{\\alpha}{2}}}, \\dfrac{|m^2+P|^{\\alpha -1}sgn(m^2+P}{\\Gamma (\\frac{\\alpha +1}{2 }} and (m^2+P±i0^{\\alpha-1}
Kernel Method for Nonlinear Granger Causality
Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2008-04-01
Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity. We develop a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces. Applications to coupled chaotic maps and physiological data sets are presented.
Finite quantum electrodynamics the causal approach
Scharf, Günter
2014-01-01
In this classic text for advanced undergraduates and graduate students of physics, author Günter Scharf carefully analyzes the role of causality in quantum electrodynamics. His approach offers full proofs and detailed calculations of scattering processes in a mathematically rigorous manner. This third edition contains Scharf's revisions and corrections plus a brief new Epilogue on gauge invariance of quantum electrodynamics to all orders. The book begins with Dirac's theory, followed by the quantum theory of free fields and causal perturbation theory, a powerful method that avoids ultraviolet divergences and solves the infrared problem by means of the adiabatic limit. Successive chapters explore properties of the S-matrix — such as renormalizability, gauge invariance, and unitarity — the renormalization group, and interactive fields. Additional topics include electromagnetic couplings and the extension of the methods to non-abelian gauge theories. Each chapter is supplemented with problems, and four appe...
Individuals with currently untreated mental illness: causal beliefs and readiness to seek help.
Stolzenburg, S; Freitag, S; Evans-Lacko, S; Speerforck, S; Schmidt, S; Schomerus, G
2018-01-16
Many people with mental illness do not seek professional help. Beliefs about the causes of their current health problem seem relevant for initiating treatment. Our aim was to find out to what extent the perceived causes of current untreated mental health problems determine whether a person considers herself/himself as having a mental illness, perceives need for professional help and plans to seek help in the near future. In a cross-sectional study, we examined 207 untreated persons with a depressive syndrome, all fulfilling criteria for a current mental illness as confirmed with a structured diagnostic interview (Mini International Neuropsychiatric Interview). The sample was recruited in the community using adverts, flyers and social media. We elicited causal explanations for the present problem, depression literacy, self-identification as having a mental illness, perceived need for professional help, help-seeking intentions, severity of depressive symptoms (Patient Health Questionnaire - Depression), and whether respondents had previously sought mental healthcare. Most participants fulfilled diagnostic criteria for a mood disorder (n = 181, 87.4%) and/or neurotic, stress-related and somatoform disorders (n = 120, 58.0%) according to the ICD-10. N = 94 (45.4%) participants had never received mental health treatment previously. Exploratory factor analysis of a list of 25 different causal explanations resulted in five factors: biomedical causes, person-related causes, childhood trauma, current stress and unhealthy behaviour. Attributing the present problem to biomedical causes, person-related causes, childhood trauma and stress were all associated with stronger self-identification as having a mental illness. In persons who had never received mental health treatment previously, attribution to biomedical causes was related to greater perceived need and stronger help-seeking intentions. In those with treatment experience, lower attribution to person-related causes and
Noise resistance of the violation of local causality for pure three-qutrit entangled states
Laskowski, Wiesław; Ryu, Junghee; Żukowski, Marek
2014-10-01
Bell's theorem started with two qubits (spins 1/2). It is a ‘no-go’ statement on classical (local causal) models of quantum correlations. After 25 years, it turned out that for three qubits the situation is even more astonishing. General statements concerning higher dimensional systems, qutrits, etc, started to appear even later, once the picture with spin (higher than 1/2) was replaced by a broader one, allowing all possible observables. This work is a continuation of the Gdansk effort to take advantage of the fact that Bell's theorem can be put in the form of a linear programming problem, which in turn can be translated into a computer code. Our results are numerical and classify the strength of the violation of local causality by various families of three-qutrit states, as measured by the resistance to noise. This is previously uncharted territory. The results may be helpful in suggesting which three-qutrit states will be handy for applications in quantum information protocols. One of the surprises is that the W state turns out to reveal a stronger violation of local causality than the GHZ (Greenberger-Horne-Zeilinger) state. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘50 years of Bell's theorem’.
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
On the causality relations in thermoelectricity
Vázquez, Federico; López de Haro, Mariano; Figueroa, Aldo
2018-01-01
The relationship between the causality principle and the existence of couplings between different thermodynamic driving forces in a given phenomenon is discussed. The case of thermoelectricity is explicitly analyzed. A transport equation for the propagation of thermal disturbances in a sample after an electric potential difference is applied is derived. The consequences of the non-hyperbolic character of this equation and the need for investigating its possible connection with nonequilibrium thermodynamics formulations are pointed out.
Causal Relationship between Construction Production and GDP in Turkey
Hakkı Kutay Bolkol
2015-01-01
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). Find...
Beyond words: Pragmatic inference in behavioral variant of frontotemporal degeneration.
Spotorno, Nicola; McMillan, Corey T; Rascovsky, Katya; Irwin, David J; Clark, Robin; Grossman, Murray
2015-08-01
When the message of a speaker goes beyond the literal or logical meaning of the sentences used, a pragmatic inference is required to understand the complete meaning of an utterance. Here we study one example of pragmatic inference, called scalar implicature. Such an inference is required when a weaker term "some" is used in a sentence like "Some of the students passed the exam" because the speaker presumably had a reason not to use a stronger term like "all". We investigated the comprehension of scalar implicatures in a group of 17 non-aphasic patients with behavioral variant frontotemporal degeneration (bvFTD) in order to test the contribution of non-linguistic decision-making ability and the role of prefrontal cortex in supporting the computation of pragmatic inferences. The results of two experiments point to a deficit in producing alternative interpretations beyond a logical reading. bvFTD patients thus prefer the narrowly literal or logical interpretation of a scalar term when they must generate a possible alternative interpretation by themselves, but patients prefer a pragmatic reading when offered a choice between the logical and the pragmatic interpretation of the same sentence. An imaging analysis links bvFTD patients' spontaneous tendency toward a narrowly logical interpretation with atrophy in ventromedial prefrontal cortex. Our findings are consistent with the pragmatic tolerance hypothesis, which proposes that difficulty generating alternative interpretations of an utterance, rather than a frank inability to compute an inference, affects the comprehension of a scalar term. Copyright © 2015 Elsevier Ltd. All rights reserved.
A new approach to causality in the frequency domain
Mehmet Dalkir
2004-01-01
This study refers to the earlier work of analysis in the frequency domain. A different definition of causality is made, and its implications to the general idea of causality are discussed. The causality relationship between two monetary aggregates, simple sum and Divisia indices, and their relation with the personal income is analyzed using wavelet time-scale decomposition.
A Quantitative Causal Model Theory of Conditional Reasoning
Fernbach, Philip M.; Erb, Christopher D.
2013-01-01
The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability…
How to Be Causal: Time, Spacetime and Spectra
Kinsler, Paul
2011-01-01
I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…
Pathway Analysis and the Search for Causal Mechanisms
Weller, Nicholas; Barnes, Jeb
2016-01-01
The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting…
Causal structure in categorical quantum mechanics
Lal, Raymond Ashwin
Categorical quantum mechanics is a way of formalising the structural features of quantum theory using category theory. It uses compound systems as the primitive notion, which is formalised by using symmetric monoidal categories. This leads to an elegant formalism for describing quantum protocols such as quantum teleportation. In particular, categorical quantum mechanics provides a graphical calculus that exposes the information flow of such protocols in an intuitive way. However, the graphical calculus also reveals surprising features of these protocols; for example, in the quantum teleportation protocol, information appears to flow `backwards-in-time'. This leads to question of how causal structure can be described within categorical quantum mechanics, and how this might lead to insight regarding the structural compatibility between quantum theory and relativity. This thesis is concerned with the project of formalising causal structure in categorical quantum mechanics. We begin by studying an abstract view of Bell-type experiments, as described by `no-signalling boxes', and we show that under time-reversal no-signalling boxes generically become signalling. This conflicts with the underlying symmetry of relativistic causal structure. This leads us to consider the framework of categorical quantum mechanics from the perspective of relativistic causal structure. We derive the properties that a symmetric monoidal category must satisfy in order to describe systems in such a background causal structure. We use these properties to define a new type of category, and this provides a formal framework for describing protocols in spacetime. We explore this new structure, showing how it leads to an understanding of the counter-intuitive information flow of protocols in categorical quantum mechanics. We then find that the formal properties of our new structure are naturally related to axioms for reconstructing quantum theory, and we show how a reconstruction scheme based on
Reiter, Johannes G; Hilbe, Christian; Rand, David G; Chatterjee, Krishnendu; Nowak, Martin A
2018-02-07
Direct reciprocity is a mechanism for cooperation among humans. Many of our daily interactions are repeated. We interact repeatedly with our family, friends, colleagues, members of the local and even global community. In the theory of repeated games, it is a tacit assumption that the various games that a person plays simultaneously have no effect on each other. Here we introduce a general framework that allows us to analyze "crosstalk" between a player's concurrent games. In the presence of crosstalk, the action a person experiences in one game can alter the person's decision in another. We find that crosstalk impedes the maintenance of cooperation and requires stronger levels of forgiveness. The magnitude of the effect depends on the population structure. In more densely connected social groups, crosstalk has a stronger effect. A harsh retaliator, such as Tit-for-Tat, is unable to counteract crosstalk. The crosstalk framework provides a unified interpretation of direct and upstream reciprocity in the context of repeated games.
DEFF Research Database (Denmark)
Heydorn, S; Andersen, K E; Johansen, J D
2003-01-01
elicitation reaction than patch testing with the allergen (hydroxycitronellal) alone, in patients previously patch tested positive to hydroxycitronellal. A stronger patch test elicitation reaction was defined as at least 1 day of patch test reading showing more positive patch tests......Household and cleaning products often contain both allergens and irritants. The aim of this double-blinded, randomized, paired study was to determine whether patch testing with an allergen (hydroxycitronellal) combined with an irritant [sodium lauryl sulfate (SLS)] cause a stronger patch test...... (+, ++ or +++) on the forearm patch tested with 6 concentrations of SLS plus hydroxycitronellal than on the forearm tested with 6 concentrations of hydroxycitronellal alone and no day of patch test readings showing more positive tests on the hydroxycitronellal forearm. 15/20 (75%) had at least 1 day of patch test reading...
Daytime warming has stronger negative effects on soil nematodes than night-time warming
Yan, Xiumin; Wang, Kehong; Song, Lihong; Wang, Xuefeng; Wu, Donghui
2017-01-01
Warming of the climate system is unequivocal, that is, stronger warming during night-time than during daytime. Here we focus on how soil nematodes respond to the current asymmetric warming. A field infrared heating experiment was performed in the western of the Songnen Plain, Northeast China. Three warming modes, i.e. daytime warming, night-time warming and diurnal warming, were taken to perform the asymmetric warming condition. Our results showed that the daytime and diurnal warming treatmen...
World Bank: Management Controls Stronger, But Challenges in Fighting Corruption Remain
2000-04-01
outline for possible World Development Report on institutions, including corruption . Completed • Prepare Europe and Central Asia Region...Management Controls Stronger, but Challenges in Fighting Corruption Remain If , 20000417 062 G A O Accountability * Integrity * Reliability GAO... corruption —broadly defined as the abuse of public office for private gain— ’The "World Bank" and "Bank" refer to the World Bank Group of institutions
A stronger version of matrix convexity as applied to functions of Hermitian matrices
Directory of Open Access Journals (Sweden)
Kagan Abram
1999-01-01
Full Text Available A stronger version of matrix convexity, called hyperconvexity is introduced. It is shown that the function is hyperconvex on the set of Hermitian matrices and is hyperconvex on the set of positive definite Hermitian matrices. The new concept makes it possible to consider weighted averages of matrices of different orders. Proofs use properties of the Fisher information matrix, a fundamental concept of mathematical statistics.
Extracting causal relationships from Chinese written text
Liu, X; Hoede, C.
2002-01-01
Expert systems form one of the most important research areas in Artificial Intelligence. The main parts in expert systems are knowledge bases and inference engines. In the knowledge bases the main knowledge is knowledge in the form of ``IF-THEN" statements. In knowledge graphs, a new form of
Locative inferences in medical texts.
Mayer, P S; Bailey, G H; Mayer, R J; Hillis, A; Dvoracek, J E
1987-06-01
Medical research relies on epidemiological studies conducted on a large set of clinical records that have been collected from physicians recording individual patient observations. These clinical records are recorded for the purpose of individual care of the patient with little consideration for their use by a biostatistician interested in studying a disease over a large population. Natural language processing of clinical records for epidemiological studies must deal with temporal, locative, and conceptual issues. This makes text understanding and data extraction of clinical records an excellent area for applied research. While much has been done in making temporal or conceptual inferences in medical texts, parallel work in locative inferences has not been done. This paper examines the locative inferences as well as the integration of temporal, locative, and conceptual issues in the clinical record understanding domain by presenting an application that utilizes two key concepts in its parsing strategy--a knowledge-based parsing strategy and a minimal lexicon.
York eHagmayer; Neele eEngelmann
2014-01-01
Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic lite...
Object-Oriented Type Inference
DEFF Research Database (Denmark)
Schwartzbach, Michael Ignatieff; Palsberg, Jens
1991-01-01
We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op-timizing......We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...
Eight challenges in phylodynamic inference
Directory of Open Access Journals (Sweden)
Simon D.W. Frost
2015-03-01
Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.
Jeong, Allan; Lee, Woon Jee
2012-01-01
This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…
Statistical Inference for Fractional Diffusion Processes
Rao, B L S Prakasa
2010-01-01
Statistical Inference for Fractional Diffusion Processes looks at statistical inference for stochastic processes modeled by stochastic differential equations driven by fractional Brownian motion. Other related processes, such as sequential inference, nonparametric and non parametric inference and parametric estimation are also discussed. The book will deal with Fractional Diffusion Processes (FDP) in relation to statistical influence for stochastic processes. The books main focus is on parametric and non parametric inference problems for fractional diffusion processes when a complete path of t
Information causality from an entropic and a probabilistic perspective
International Nuclear Information System (INIS)
Al-Safi, Sabri W.; Short, Anthony J.
2011-01-01
The information causality principle is a generalization of the no-signaling principle which implies some of the known restrictions on quantum correlations. But despite its clear physical motivation, information causality is formulated in terms of a rather specialized game and figure of merit. We explore different perspectives on information causality, discussing the probability of success as the figure of merit, a relation between information causality and the nonlocal ''inner-product game,'' and the derivation of a quadratic bound for these games. We then examine an entropic formulation of information causality with which one can obtain the same results, arguably in a simpler fashion.
Particulate air pollution and increased mortality: Biological plausibility for causal relationship
International Nuclear Information System (INIS)
Henderson, R.F.
1995-01-01
Recently, a number of epidemiological studies have concluded that ambient particulate exposure is associated with increased mortality and morbidity at PM concentrations well below those previously thought to affect human health. These studies have been conducted in several different geographical locations and have involved a range of populations. While the consistency of the findings and the presence of an apparent concentration response relationship provide a strong argument for causality, epidemiological studies can only conclude this based upon inference from statistical associations. The biological plausibility of a causal relationship between low concentrations of PM and daily mortality and morbidity rates is neither intuitively obvious nor expected based on past experimental studies on the toxicity of inhaled particles. Chronic toxicity from inhaled, poorly soluble particles has been observed based on the slow accumulation of large lung burdens of particles, not on small daily fluctuations in PM levels. Acute toxicity from inhaled particles is associated mainly with acidic particles and is observed at much higher concentrations than those observed in the epidemiology studies reporting an association between PM concentrations and morbidity/mortality. To approach the difficult problem of determining if the association between PM concentrations and daily morbidity and mortality is biologically plausible and causal, one must consider (1) the chemical and physical characteristics of the particles in the inhaled atmospheres, (2) the characteristics of the morbidity/mortality observed and the people who are affected, and (3) potential mechanisms that might link the two
Kazak, Sibel; Pratt, Dave
2017-01-01
This study considers probability models as tools for both making informal statistical inferences and building stronger conceptual connections between data and chance topics in teaching statistics. In this paper, we aim to explore pre-service mathematics teachers' use of probability models for a chance game, where the sum of two dice matters in…
Processing of Scalar Inferences by Mandarin Learners of English: An Online Measure.
Lin, Yowyu
2016-01-01
Scalar inferences represent the condition when a speaker uses a weaker expression such as some in a pragmatic scale like , and s/he has the intention to reject the stronger use of the other word like all in the utterance. Considerable disagreement has arisen concerning how interlocutors derive the inferences. The study presented here tries to address this issue by examining online scalar inferences among Mandarin learners of English. To date, Default Inference and Relevance Theory have made different predictions regarding how people process scalar inferences. Findings from recently emerging first language studies did not fully resolved the debate but led to even more heated debates. The current three online psycholinguistic experiments reported here tried to address the processing of scalar inferences from second language perspective. Results showed that Mandarin learners of English showed faster reaction times and a higher acceptance rate when interpreting some as some but not all and this was true even when subjects were under time pressure, which was manifested in Experiment 2. Overall, the results of the experiments supported Default Theory. In addition, Experiment 3 also found that working memory capacity plays a critical role during scalar inference processing. High span readers were faster in accepting the some but not all interpretation than low span readers. However, compared with low span readers, high span readers were more likely to accept the some and possibly all condition, possibly due to their working memory capacity to generate scenarios to fit the interpretation.
Processing of Scalar Inferences by Mandarin Learners of English: An Online Measure.
Directory of Open Access Journals (Sweden)
Yowyu Lin
Full Text Available Scalar inferences represent the condition when a speaker uses a weaker expression such as some in a pragmatic scale like , and s/he has the intention to reject the stronger use of the other word like all in the utterance. Considerable disagreement has arisen concerning how interlocutors derive the inferences. The study presented here tries to address this issue by examining online scalar inferences among Mandarin learners of English. To date, Default Inference and Relevance Theory have made different predictions regarding how people process scalar inferences. Findings from recently emerging first language studies did not fully resolved the debate but led to even more heated debates. The current three online psycholinguistic experiments reported here tried to address the processing of scalar inferences from second language perspective. Results showed that Mandarin learners of English showed faster reaction times and a higher acceptance rate when interpreting some as some but not all and this was true even when subjects were under time pressure, which was manifested in Experiment 2. Overall, the results of the experiments supported Default Theory. In addition, Experiment 3 also found that working memory capacity plays a critical role during scalar inference processing. High span readers were faster in accepting the some but not all interpretation than low span readers. However, compared with low span readers, high span readers were more likely to accept the some and possibly all condition, possibly due to their working memory capacity to generate scenarios to fit the interpretation.
Elkins, Irene J; Saunders, Gretchen R B; Malone, Stephen M; Keyes, Margaret A; McGue, Matt; Iacono, William G
2018-03-01
We report whether the etiology underlying associations of childhood ADHD with adolescent alcohol and marijuana involvement is consistent with causal relationships or shared predispositions, and whether it differs by gender. In three population-based twin samples (N = 3762; 64% monozygotic), including one oversampling females with ADHD, regressions were conducted with childhood inattentive or hyperactive-impulsive symptoms predicting alcohol and marijuana outcomes by age 17. To determine whether ADHD effects were consistent with causality, twin difference analyses divided effects into those shared between twins in the pair and those differing within pairs. Adolescents with more severe childhood ADHD were more likely to initiate alcohol and marijuana use earlier, escalate to frequent or heavy use, and develop symptoms. While risks were similar across genders, females with more hyperactivity-impulsivity had higher alcohol consumption and progressed further toward daily marijuana use than did males. Monozygotic twins with more severe ADHD than their co-twins did not differ significantly on alcohol or marijuana outcomes, however, suggesting a non-causal relationship. When co-occurring use of other substances and conduct/oppositional defiant disorders were considered, hyperactivity-impulsivity remained significantly associated with both substances, as did inattention with marijuana, but not alcohol. Childhood ADHD predicts when alcohol and marijuana use are initiated and how quickly use escalates. Shared familial environment and genetics, rather than causal influences, primarily account for these associations. Stronger relationships between hyperactivity-impulsivity and heavy drinking/frequent marijuana use among adolescent females than males, as well as the greater salience of inattention for marijuana, merit further investigation. Copyright © 2017 Elsevier B.V. All rights reserved.
Emergent Geometry from Entropy and Causality
Engelhardt, Netta
In this thesis, we investigate the connections between the geometry of spacetime and aspects of quantum field theory such as entanglement entropy and causality. This work is motivated by the idea that spacetime geometry is an emergent phenomenon in quantum gravity, and that the physics responsible for this emergence is fundamental to quantum field theory. Part I of this thesis is focused on the interplay between spacetime and entropy, with a special emphasis on entropy due to entanglement. In general spacetimes, there exist locally-defined surfaces sensitive to the geometry that may act as local black hole boundaries or cosmological horizons; these surfaces, known as holographic screens, are argued to have a connection with the second law of thermodynamics. Holographic screens obey an area law, suggestive of an association with entropy; they are also distinguished surfaces from the perspective of the covariant entropy bound, a bound on the total entropy of a slice of the spacetime. This construction is shown to be quite general, and is formulated in both classical and perturbatively quantum theories of gravity. The remainder of Part I uses the Anti-de Sitter/ Conformal Field Theory (AdS/CFT) correspondence to both expand and constrain the connection between entanglement entropy and geometry. The AdS/CFT correspondence posits an equivalence between string theory in the "bulk" with AdS boundary conditions and certain quantum field theories. In the limit where the string theory is simply classical General Relativity, the Ryu-Takayanagi and more generally, the Hubeny-Rangamani-Takayanagi (HRT) formulae provide a way of relating the geometry of surfaces to entanglement entropy. A first-order bulk quantum correction to HRT was derived by Faulkner, Lewkowycz and Maldacena. This formula is generalized to include perturbative quantum corrections in the bulk at any (finite) order. Hurdles to spacetime emergence from entanglement entropy as described by HRT and its quantum
Inference Optimization using Relational Algebra
Evers, S.; Fokkinga, M.M.; Apers, Peter M.G.
Exact inference procedures in Bayesian networks can be expressed using relational algebra; this provides a common ground for optimizations from the AI and database communities. Specifically, the ability to accomodate sparse representations of probability distributions opens up the way to optimize
Mixed normal inference on multicointegration
Boswijk, H.P.
2009-01-01
Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the
Statistical inference on variance components
Verdooren, L.R.
1988-01-01
In several sciences but especially in animal and plant breeding, the general mixed model with fixed and random effects plays a great role. Statistical inference on variance components means tests of hypotheses about variance components, constructing confidence intervals for them, estimating them,
A Causal Theory of Mnemonic Confabulation.
Bernecker, Sven
2017-01-01
This paper attempts to answer the question of what defines mnemonic confabulation vis-à-vis genuine memory. The two extant accounts of mnemonic confabulation as "false memory" and as ill-grounded memory are shown to be problematic, for they cannot account for the possibility of veridical confabulation, ill-grounded memory, and well-grounded confabulation. This paper argues that the defining characteristic of mnemonic confabulation is that it lacks the appropriate causal history. In the confabulation case, there is no proper counterfactual dependence of the state of seeming to remember on the corresponding past representation.
A Causal Theory of Mnemonic Confabulation
Directory of Open Access Journals (Sweden)
Sven Bernecker
2017-07-01
Full Text Available This paper attempts to answer the question of what defines mnemonic confabulation vis-à-vis genuine memory. The two extant accounts of mnemonic confabulation as “false memory” and as ill-grounded memory are shown to be problematic, for they cannot account for the possibility of veridical confabulation, ill-grounded memory, and well-grounded confabulation. This paper argues that the defining characteristic of mnemonic confabulation is that it lacks the appropriate causal history. In the confabulation case, there is no proper counterfactual dependence of the state of seeming to remember on the corresponding past representation.
De Broglie's causal interpretations of quantum mechanics
International Nuclear Information System (INIS)
YOAV Ben-Dov
1989-01-01
In this article we trace the history of de Broglie's two causal interpretations of quantum mechanics, namely the double solution and the pilot wave theories, at the two periods in which he developed them: 1924-27 and 1952 onwards. Examining the reasons for which he always preferred the first theory to the second, reasons that are mainly concerned with the question of the physical nature of the quantum wave function, we try to show the continuity and the coherence of his underlying vision
[Therapy of polyneuropathies. Causal and symptomatic].
Müller-Felber, W
2001-05-28
In the first instance, polyneuropathies are treated causally. The most common underlying cause is diabetes mellitus or alcohol abuse. In a large number of patients with polyneuropathy, however, the underlying cause cannot be definitively identified. For these--but equally for patients with etiologically clear polyneuropathy--a stock-taking of clinical symptoms should be carried out and, where indicated, symptomatic treatment initiated. In addition to medication aimed at combating pain, muscular spasm, autonomic functional disorders, and for the prevention of thrombosis, physical measures (physiotherapy, foot care, orthopedic shoes) are of primary importance.
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
Kaltiala-Heino, Riittakerttu; Fröjd, Sari; Marttunen, Mauri
2016-08-01
To study the associations between subjection to sexual harassment and emotional (depression) and behavioural (delinquency) symptoms among 14-to-18-year-old adolescents, and gender differences within these associations. 90,953 boys and 91,746 girls aged 14-18 participated in the School Health Promotion Study (SHPS), a school-based survey designed to examine the health, health behaviours, and school experiences of teenagers. Experiences of sexual harassment were elicited with five questions addressing five separate forms of harassment. Depression was measured by the 13-item Beck Depression Inventory and delinquency with a modified version of the International Self-Report Delinquency Study (ISRD) instrument. Data were analysed using cross-tabulations with Chi-square statistics and logistic regression. All sexual harassment experiences studied were associated with both depression (adjusted odds ratios varied from 2.2 to 2.7 in girls and from 2.0 to 5.1 in boys) and delinquency (adjusted odds ratios 3.1-5.0 in girls and 1.7-6.9 in boys). Sexual name-calling had a stronger association with depression and with delinquency in girls (adjusted odds ratios, respectively, 2.4 and 4.2), than in boys (adjusted odds ratios, respectively, 2.0 and 1.7), but otherwise stronger associations with emotional and behavioural symptoms were seen in boys. Subjection to sexual harassment is associated with both emotional and behavioural symptoms in both girls and boys. The associations are mostly stronger for boys. Boys subjected to sexual harassment may feel particularly threatened regarding their masculinity, and there may be less support available for boys traumatised due to sexual harassment.
All of statistics a concise course in statistical inference
Wasserman, Larry
2004-01-01
This book is for people who want to learn probability and statistics quickly It brings together many of the main ideas in modern statistics in one place The book is suitable for students and researchers in statistics, computer science, data mining and machine learning This book covers a much wider range of topics than a typical introductory text on mathematical statistics It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses The reader is assumed to know calculus and a little linear algebra No previous knowledge of probability and statistics is required The text can be used at the advanced undergraduate and graduate level Larry Wasserman is Professor of Statistics at Carnegie Mellon University He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bi...
Building Stronger State Energy Partnerships with the U.S. Department of Energy
Energy Technology Data Exchange (ETDEWEB)
David Terry
2008-09-30
This final technical report details the results of total work efforts and progress made from July 2000 - July 2008 under the National Association of State Energy Officials (NASEO) cooperative agreement DE-FC26-00NT40802, Building Stronger State Energy Partnerships with the U.S. Department of Energy. Major topical project areas in this final report include work efforts in the following areas: Rebuild America/Energy Smart Schools, Higher Education Initiative, Winter/Summer Fuels Outlook Conferences, Energy Emergency, Clean Energy Integration, Energy Star, and Office of Electricity Delivery and Energy Reliability. All required deliverables have been provided to the National Energy Technology Laboratory and DOE program officials.
Behrns, Kevin E; Copeland, Edward M; Howard, Richard J
2012-01-01
Established in 1957, the University of Florida Department of Surgery has a solid foundation on which current faculty are driven to build a stronger tomorrow. The department is focused on promoting patient-centered care, expanding its research portfolio to improve techniques and outcomes, and training the surgical leaders of tomorrow. It fosters an environment where faculty, residents, students, and staff challenge long-held traditions with the goal of improving the health of our patients, the quality of our care, and the vitality of our work environment.
International Nuclear Information System (INIS)
Silva Filho, Elias
1999-01-01
The use of gamma radiation to obtain wood-polymer composites is one of the applications of radiation that presents the most commercial interest. The process, denominated radiopolymerization, comprises the impregnation of monomers into the completely dried wood followed by exposure to gamma radiation to induce polymerization of the impregnated monomers. I this context, the present work aimed the application of this process to seven kinds of wood existing in the brazilian forests. The considered monomer is styrene and the gamma source is Cobalt-60. The obtained wood-polystyrene composites were found to have stronger static bending strength. (author)
Building Stronger State Energy Partnerships with the U.S. Department of Energy
Energy Technology Data Exchange (ETDEWEB)
Marks, Kate
2011-09-30
This final technical report details the results of total work efforts and progress made from October 2007 – September 2011 under the National Association of State Energy Officials (NASEO) cooperative agreement DE-FC26-07NT43264, Building Stronger State Energy Partnerships with the U.S. Department of Energy. Major topical project areas in this final report include work efforts in the following areas: Energy Assurance and Critical Infrastructure, State and Regional Technical Assistance, Regional Initiative, Regional Coordination and Technical Assistance, and International Activities in China. All required deliverables have been provided to the National Energy Technology Laboratory and DOE program officials.
Directory of Open Access Journals (Sweden)
Huang Chia-Ling
2012-03-01
Full Text Available Abstract Background Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. Results We present a simple data-driven method, Correlation Set Analysis (CSA, for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. Conclusions CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.
Foundational perspectives on causality in large-scale brain networks
Mannino, Michael; Bressler, Steven L.
2015-12-01
A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical
Are bruxism and the bite causally related?
Lobbezoo, F; Ahlberg, J; Manfredini, D; Winocur, E
2012-07-01
In the dental profession, the belief that bruxism and dental (mal-)occlusion ('the bite') are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query 'Bruxism [Majr] AND (Dental Occlusion [Majr] OR Malocclusion [Majr])', yielded 93 articles, of which 46 papers were finally included in the present review*. Part of the included publications dealt with the possible associations between bruxism and aspects of occlusion, from which it was concluded that neither for occlusal interferences nor for factors related to the anatomy of the oro-facial skeleton, there is any evidence available that they are involved in the aetiology of bruxism. Instead, there is a growing awareness of other factors (viz. psychosocial and behavioural ones) being important in the aetiology of bruxism. Another part of the included papers assessed the possible mediating role of occlusion between bruxism and its purported consequences (e.g. tooth wear, loss of periodontal tissues, and temporomandibular pain and dysfunction). Even though most dentists agree that bruxism may have several adverse effects on the masticatory system, for none of these purported adverse effects, evidence for a mediating role of occlusion and articulation has been found to date. Hence, based on this review, it should be concluded that to date, there is no evidence whatsoever for a causal relationship between bruxism and the bite. © 2012 Blackwell Publishing Ltd.
[Antibibiotic resistance by nosocomial infections' causal agents].
Salazar-Holguín, Héctor Daniel; Cisneros-Robledo, María Elena
2016-01-01
The antibibiotic resistance by nosocomial infections (NI) causal agents constitutes a seriously global problematic that involves the Mexican Institute of Social Security's Regional General Hospital 1 in Chihuahua, Mexico; although with special features that required to be specified and evaluated, in order to concrete an effective therapy. Observational, descriptive and prospective study; by means of active vigilance all along 2014 in order to detect the nosocomial infections, for epidemiologic study, culture and antibiogram to identify its causal agents and antibiotics resistance and sensitivity. Among 13527 hospital discharges, 1079 displayed NI (8 %), standed out: the related on vascular lines, of surgical site, pneumonia and urinal track; they added up two thirds of the total. We carried out culture and antibiogram about 300 of them (27.8 %); identifying 31 bacterian species, mainly seven of those (77.9 %): Escherichia coli, Staphylococcus aureus and epidermidis, Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae and Enterobacter cloacae; showing multiresistance to 34 tested antibiotics, except in seven with low or without resistance at all: vancomycin, teicoplanin, linezolid, quinupristin-dalfopristin, piperacilin-tazobactam, amikacin and carbapenems. When we contrasted those results with the recommendations in the clinical practice guides, it aroused several contradictions; so they must be taken with reserves and has to be tested in each hospital, by means of cultures and antibiograms in practically every case of nosocomial infection.
Evidence for online processing during causal learning.
Liu, Pei-Pei; Luhmann, Christian C
2015-03-01
Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.
Diagnostic reasoning using qualitative causal models
International Nuclear Information System (INIS)
Sudduth, A.L.
1992-01-01
The application of expert systems to reasoning problems involving real-time data from plant measurements has been a topic of much research, but few practical systems have been deployed. One obstacle to wider use of expert systems in applications involving real-time data is the lack of adequate knowledge representation methodologies for dynamic processes. Knowledge bases composed mainly of rules have disadvantages when applied to dynamic processes and real-time data. This paper describes a methodology for the development of qualitative causal models that can be used as knowledge bases for reasoning about process dynamic behavior. These models provide a systematic method for knowledge base construction, considerably reducing the engineering effort required. They also offer much better opportunities for verification and validation of the knowledge base, thus increasing the possibility of the application of expert systems to reasoning about mission critical systems. Starting with the Signed Directed Graph (SDG) method that has been successfully applied to describe the behavior of diverse dynamic processes, the paper shows how certain non-physical behaviors that result from abstraction may be eliminated by applying causal constraint to the models. The resulting Extended Signed Directed Graph (ESDG) may then be compiled to produce a model for use in process fault diagnosis. This model based reasoning methodology is used in the MOBIAS system being developed by Duke Power Company under EPRI sponsorship. 15 refs., 4 figs
Introducing mechanics by tapping core causal knowledge
International Nuclear Information System (INIS)
Klaassen, Kees; Westra, Axel; Emmett, Katrina; Eijkelhof, Harrie; Lijnse, Piet
2008-01-01
This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force causes a deviation from how an object would move of its own accord (i.e. its force-free motion), and (b) an incentive to search, where the motion deviates from the assumed force-free motion, for recurring configurations with which such deviations can be correlated (interaction theory). Various assumptions can be made concerning both the force-free motion and the interaction theory, thus giving rise to a variety of specific explanations. Kepler's semi-implicit intuition about the force-free motion is rest, Newton's explicit assumption is uniform rectilinear motion, while in everyday explanations a diversity of pragmatic suggestions can be recognized. The idea is that the explanatory strategy, once made explicit by drawing on students' intuitive causal knowledge, can be made to function for students as an advance organizer, in the sense of a general scheme that they recognize but do not yet know how to detail for scientific purposes
de Irala, Jokin; Ruiz-Canela, Miguel; Martínez-González, Miguel Angel
2005-12-01
The aim of this paper is to update and critically analyze the public health relevance of available evidence about the causal relationship between cannabis use and psychosis or depression. There are conflicting views about this causal relationship. Two systematic reviews of existing evidence assessed this association and were published in 2004, but they came to different conclusions. From a public health perspective a thorough discussion is warranted before attributing any observed effect to potential biases. First, the impact of residual confounding in this causal relationship is discussed. We consider that the attenuation of estimates after controlling for confounding factors cannot be interpreted as evidence to support the claim that residual confounding fully explains this link. Secondly, taking into account the results of recent studies, the time-sequence and dose-response criteria of causality are discussed. The fact that unreported or subclinical psychological problems might have preceded and precipitated cannabis use is a very unlikely explanation when the cannabis-psychosis outcome link is assessed from different longitudinal studies. And finally, available evidence is interpreted with a broad view of public health and by taking into account the precautionary principle. We therefore provide reasons to support the case that stronger preventive actions against cannabis are still required in order to avoid the potential increased incidence of psychosocial health problems in the future.
Directory of Open Access Journals (Sweden)
Hannah H Leslie
Full Text Available OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC use and cervical intraepithelial neoplasia 2 or greater (CIN2+ and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7% were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9% increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an
¿CONFIEREN PODERES CAUSALES LOS UNIVERSALES TRASCENDENTES?
Directory of Open Access Journals (Sweden)
José Tomás Alvarado Marambio
2013-11-01
Full Text Available This work discusses the so-called ‘Eleatic’ argument against the existence of transcendent universals, i. e. universals which does not require instantiation for its existence. The Eleatic Principle states that everything produces a difference in the causal powers of something. As transcendent universals seem not to produce such a difference, transcendent universals seem not to exist. The argument depends crucially on the justification and the interpretation of the Eleatic Principle. It is argued, first, that it is not very clear that the principle is justified, and, second, that there are several alternatives for its interpretation, in relation with the different theories one can endorse about modality or causality. Anti-realist theories of modality or causality are not very appropriate for the understanding of what should be a ‘causal power’. Neither does a realist theory of causality conjoined with a combinatorial theory of possible worlds. A ‘causal power’ seems to be better understood in connection with a realist –non-reductionist– theory of causality and a causal theory of modality. Taken in this way the Eleatic Principle, nonetheless, it is argued that transcendent universals do ‘produce’ a difference in causal powers, for every causal connection requires such universals for its existence.
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
Quantum causality conceptual issues in the causal theory of quantum mechanics
Riggs, Peter J; French, Steven RD
2009-01-01
This is a treatise devoted to the foundations of quantum physics and the role that causality plays in the microscopic world governed by the laws of quantum mechanics. The book is controversial and will engender some lively debate on the various issues raised.
Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series
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
Directory of Open Access Journals (Sweden)
Xionghe Qin
2017-09-01
Full Text Available In this study, we bridge an important gap in the literature by comparing the extent to which external technology spillovers, as indicated by foreign direct investment (FDI, and internal technology spillovers, as indicated by university-institute-industry cooperation (UIC, influence innovation efficiency in China. We divide the innovation process into two sequential stages, namely the knowledge creation and technology commercialization stages, and employ a network data envelopment analysis approach to measure innovation efficiency at each stage. The spatial analysis of the distribution of knowledge creation efficiency and technology commercialization efficiency reveals the heterogeneity of innovation efficiency at the provincial level. Then, a panel data regression is used to analyze the effect of FDI and UIC on innovation efficiency at each stage, using data from 2009 to 2015 for 30 provinces in China. By comparing FDI with UIC, we find that FDI has a higher coefficient and stronger significance level at the knowledge creation stage, while only industry-institute linkages exhibit a stronger association with innovation efficiency at the technology commercialization stage.
The El Niño Southern Oscillation is Getting Bigger and Stronger
McNaught, C.; O'Brien, J.; Strazzo, S. E.
2012-12-01
The El Niño Southern Oscillation (ENSO) is an important natural climate variation that affects large portions of the world. We measure ENSO both in terms of its frequency and its magnitude. The different phases of ENSO - El Niño and La Niña - have different properties, and impact the global weather pattern differently. We examine the hypothesis that ENSO's frequency distribution is changing. We demonstrate that, indeed, El Niño's are getting stronger as measured by the maximum anomaly in sea surface temperature (SST). An analysis of the ENSO principal component is conducted using a fast Fourier transform to estimate the spectrum of the SST of the time series. We conclude that the intensity of El Niño events during the period 1970-2010 is statistically significantly higher when compared to the 1930—1970, with a broad spectral peak centered around 4 years. When we compare the SST spectrum for the period 1930-1970 with the spectrum for 1971- 2010, we find the latter period to be much stronger in power. Additionally recently classified ENSO types, including El Niño Modoki and Warm Pool ENSO, are briefly studied.; The first empirical orthogonal function of sea-surface temperatures (1930-2010) accounting for 75% of the variance. The values are indicative of departures from the mean, in °C. Positive (negative) values indicate anomalously higher (lower) sea-surface temperatures ; Normalized first principal component
Stronger interference from distractors in the right hemifield during visual search.
Carlei, Christophe; Kerzel, Dirk
2018-03-01
The orientation-bias hypothesis states that there is a bias to attend to the right visual hemifield (RVF) when there is spatial competition between stimuli in the left and right hemifield [Pollmann, S. (1996). A pop-out induced extinction-like phenomenon in neurologically intact subjects. Neuropsychologia, 34(5), 413-425. doi: 10.1016/0028-3932(95)00125-5 ]. In support of this hypothesis, stronger interference was reported for RVF distractors with contralateral targets. In contrast, previous studies using rapid serial visual presentation (RSVP) found stronger interference from distractors in the left visual hemifield (LVF). We used the additional singleton paradigm to test whether this discrepancy was due to the different distractor features that were employed (colour vs. orientation). Interference from the colour distractor with contralateral targets was larger in the RVF than in the LVF. However, the asymmetrical interference disappeared when observers had to search for an inconspicuous colour target instead of the inconspicuous shape target. We suggest that the LVF orienting-bias is limited to situations where search is driven by bottom-up saliency (singleton search) instead of top-down search goals (feature search). In contrast, analysis of the literature suggests the opposite for the LVF bias in RSVP tasks. Thus, the attentional asymmetry may depend on whether the task involves temporal or spatial competition, and whether search is based on bottom-up or top-down signals.
When surging seas meet stronger rain: Nuclear techniques in flood management
International Nuclear Information System (INIS)
Quevenco, Rodolfo
2015-01-01
Unusually high rainfall in many parts of the world is a result of climate change, scientists say. Since warmer air can hold more water, the rationale goes, increased temperatures will increase the chances of stronger rainfall events. And when surging seas combine with stronger rain, the outcome is almost certain: floods. Floods are the most frequently occurring natural disasters, and south-east Asia is particularly vulnerable. Climate change and variability are expected to bring about increased typhoon activities, rising sea levels and off-season monsoon rains in southeast Asia and other regions. These can cause devastating floods in countries like Cambodia, Laos, Pakistan, the Philippines, Thailand and Viet Nam. For the residents of these countries who have survived the ravages of major floods, the road to recovery can be long and arduous. As the flood water recedes, they have to contend with new forms of flood: floods of concern and worries as to how to rebuild their houses, their lives and their cities. Governments, too, face huge challenges in rebuilding roads, public buildings, infrastructure and natural resources destroyed or polluted by the flood.
Zavaglia, Melissa; Forkert, Nils D; Cheng, Bastian; Gerloff, Christian; Thomalla, Götz; Hilgetag, Claus C
2015-01-01
Lesion analysis reveals causal contributions of brain regions to mental functions, aiding the understanding of normal brain function as well as rehabilitation of brain-damaged patients. We applied a novel lesion inference technique based on game theory, Multi-perturbation Shapley value Analysis (MSA), to a large clinical lesion dataset. We used MSA to analyze the lesion patterns of 148 acute stroke patients together with their neurological deficits, as assessed by the National Institutes of Health Stroke Scale (NIHSS). The results revealed regional functional contributions to essential behavioral and cognitive functions as reflected in the NIHSS, particularly by subcortical structures. There were also side specific differences of functional contributions between the right and left hemispheric brain regions which may reflect the dominance of the left hemispheric syndrome aphasia in the NIHSS. Comparison of MSA to established lesion inference methods demonstrated the feasibility of the approach for analyzing clinical data and indicated its capability for objectively inferring functional contributions from multiple injured, potentially interacting sites, at the cost of having to predict the outcome of unknown lesion configurations. The analysis of regional functional contributions to neurological symptoms measured by the NIHSS contributes to the interpretation of this widely used standardized stroke scale in clinical practice as well as clinical trials and provides a first approximation of a 'map of stroke'.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
Statistical inference on residual life
Jeong, Jong-Hyeon
2014-01-01
This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.
Bayesian Inference on Proportional Elections
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Statistical inference an integrated approach
Migon, Helio S; Louzada, Francisco
2014-01-01
Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...
Bayesian inference on proportional elections.
Directory of Open Access Journals (Sweden)
Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
Racing for conditional independence inference
Czech Academy of Sciences Publication Activity Database
Bouckaert, R. R.; Studený, Milan
2005-01-01
Roč. 3571, - (2005), s. 221-232 ISSN 0302-9743. [ECSQARU 2005. European Conference /8./. Barcelona, 06.07.2005-08.07.2005] R&D Projects: GA ČR GA201/04/0393; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * imset * racing algorithms Subject RIV: BA - General Mathematics
On Quantum Statistical Inference, II
Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P. E.
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...
Nonparametric predictive inference in reliability
International Nuclear Information System (INIS)
Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.
2002-01-01
We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere
Entanglement, non-Markovianity, and causal non-separability
Milz, Simon; Pollock, Felix A.; Le, Thao P.; Chiribella, Giulio; Modi, Kavan
2018-03-01
Quantum mechanics, in principle, allows for processes with indefinite causal order. However, most of these causal anomalies have not yet been detected experimentally. We show that every such process can be simulated experimentally by means of non-Markovian dynamics with a measurement on additional degrees of freedom. In detail, we provide an explicit construction to implement arbitrary a causal processes. Furthermore, we give necessary and sufficient conditions for open system dynamics with measurement to yield processes that respect causality locally, and find that tripartite entanglement and nonlocal unitary transformations are crucial requirements for the simulation of causally indefinite processes. These results show a direct connection between three counter-intuitive concepts: entanglement, non-Markovianity, and causal non-separability.
A MATLAB toolbox for Granger causal connectivity analysis.
Seth, Anil K
2010-02-15
Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language. Copyright 2009 Elsevier B.V. All rights reserved.
Computational Neuropsychology and Bayesian Inference.
Parr, Thomas; Rees, Geraint; Friston, Karl J
2018-01-01
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world). This draws upon the notion of a Bayes optimal pathology - optimal inference with suboptimal priors - and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient's behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
Continuous Integrated Invariant Inference, Phase I
National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...
Variational inference & deep learning : A new synthesis
Kingma, D.P.
2017-01-01
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
Causal attribution among women with breast cancer
Directory of Open Access Journals (Sweden)
Ana Carolina W. B. Peuker
2016-01-01
Full Text Available Abstract Causal attribution among women with breast cancer was studied. The study included 157 women outpatients with breast cancer. A form for sociodemographic and clinical data and the Revised Illness Perception Questionnaire (IPQ-R were used. The results showed that women attributed breast cancer primarily to psychological causes, which does not correspond to known multifactorial causes validated by the scientific community. Providing high quality, patient-centered care requires sensitivity to breast cancer women’s beliefs about the causes of their cancer and awareness of how it can influence patient’s health behaviors after diagnosis. If women with breast cancer attribute the illness to modifiable factors then they can keep a healthy lifestyle, improving their recovery and decrease the probability of cancer recurrence after diagnosis.
Delinquency among pathological gamblers: A causal approach.
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.
Causality Constraints in Conformal Field Theory
CERN. Geneva
2015-01-01
Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d-dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂φ)4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinni...
A study in cosmology and causal thermodynamics
International Nuclear Information System (INIS)
Oliveira, H.P. de.
1986-01-01
The especial relativity of thermodynamic theories for reversible and irreversible processes in continuous medium is studied. The formalism referring to equilibrium and non-equilibrium configurations, and theories which includes the presence of gravitational fields are discussed. The nebular model in contraction with dissipative processes identified by heat flux and volumetric viscosity is thermodymically analysed. This model is presented by a plane conformal metric. The temperature, pressure, entropy and entropy production within thermodynamic formalism which adopts the hypothesis of local equilibrium, is calculated. The same analysis is carried out considering a causal thermodynamics, which establishes a local entropy of non-equilibrium. Possible homogeneous and isotropic cosmological models, considering the new phenomenological equation for volumetric viscosity deriving from cause thermodynamics are investigated. The found out models have plane spatial section (K=0) and some ones do not have singularities. The energy conditions are verified and the entropy production for physically reasobable models are calculated. (M.C.K.) [pt
On the causal set–continuum correspondence
International Nuclear Information System (INIS)
Saravani, Mehdi; Aslanbeigi, Siavash
2014-01-01
We present two results that concern certain aspects of the question: when is a causal set well approximated by a Lorentzian manifold? The first result is a theorem that shows that the number–volume correspondence, if required to hold even for arbitrarily small regions, is best realized via Poisson sprinkling. The second result concerns a family of lattices in 1+1 dimensional Minkowski space, known as Lorentzian lattices, which we show provide a much better number–volume correspondence than Poisson sprinkling for large volumes. We argue, however, that this feature should not persist in higher dimensions. We conclude by conjecturing a form of the aforementioned theorem that holds under weaker assumptions, namely that Poisson sprinkling provides the best number–volume correspondence in 3+1 dimensions for spacetime regions with macroscopically large volumes. (paper)
Exploring Torus Universes in Causal Dynamical Triangulations
DEFF Research Database (Denmark)
Budd, Timothy George; Loll, R.
2013-01-01
Motivated by the search for new observables in nonperturbative quantum gravity, we consider Causal Dynamical Triangulations (CDT) in 2+1 dimensions with the spatial topology of a torus. This system is of particular interest, because one can study not only the global scale factor, but also global ....... Apart from setting the stage for the analysis of shape dynamics on the torus, the new set-up highlights the role of nontrivial boundaries and topology....... shape variables in the presence of arbitrary quantum fluctuations of the geometry. Our initial investigation focusses on the dynamics of the scale factor and uncovers a qualitatively new behaviour, which leads us to investigate a novel type of boundary conditions for the path integral. Comparing large...
Equity Theory Ratios as Causal Schemas.
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.
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.
Hernández, Ingrid; Portieles, Roxana; Rodríguez García, Mayra; López, Yunior; Aranguren, Miguel; Alonso, Eugenio; Delgado, Roger; Luis, Maritza; Batista, Lochy; Paredes, Camilo; Rodríguez, Meilyn; Pujol, Merardo; Ochagavia, María Elena; Falcón, Viviana; Terauchi, Ryohei; Matsumura, Hideo; Ayra-Pardo, Camilo; Llauger, Raixa; Pérez, María del Carmen; Núñez, Mirian; Borrusch, Melissa S.; Walton, Jonathan D.; Silva, Yussuan; Pimentel, Eulogio; Borroto, Carlos; Borrás-Hidalgo, Orlando
2016-01-01
Huanglongbing (HLB) constitutes the most destructive disease of citrus worldwide, yet no established efficient management measures exist for it. Brassinosteroids, a family of plant steroidal compounds, are essential for plant growth, development and stress tolerance. As a possible control strategy for HLB, epibrassinolide was applied to as a foliar spray to citrus plants infected with the causal agent of HLB, ‘Candidatus Liberibacter asiaticus’. The bacterial titers were reduced after treatment with epibrassinolide under both greenhouse and field conditions but were stronger in the greenhouse. Known defense genes were induced in leaves by epibrassinolide. With the SuperSAGE technology combined with next generation sequencing, induction of genes known to be associated with defense response to bacteria and hormone transduction pathways were identified. The results demonstrate that epibrassinolide may provide a useful tool for the management of HLB. PMID:26731660
Causal analysis of self-sustaining processes in the log-layer of wall-bounded turbulence
Lozano-Duran, Adrian; Bae, Hyunji Jane
2017-11-01
Despite the large amount of information provided by direct numerical simulations of turbulent flows, the underlying dynamics remain elusive even in the most simple and canonical configurations. Most standard methods used to investigate turbulence do not provide a clear causal inference between events, which is necessary to determine this dynamics, particularly in self-sustaning processes. In the present work, we examine the causal interactions between streaks and rolls in the logarithmic layer of minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. Streaks are represented by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocities. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear, which controls the dynamics and time-scales. The well-known lift-up effect is shown to be not a key ingredient in the causal network between shear, streaks and rolls. Funded by ERC Coturb Madrid Summer Program.
Causal topology in future and past distinguishing spacetimes
Parrikar, Onkar; Surya, Sumati
2011-08-01
The causal structure of a strongly causal spacetime is particularly well endowed. Not only does it determine the conformal spacetime geometry when the spacetime dimension n > 2, as shown by Malament and Hawking-King-McCarthy (MHKM), but also the manifold dimension. The MHKM result, however, applies more generally to spacetimes satisfying the weaker causality condition of future and past distinguishability (FPD), and it is an important question whether the causal structure of such spacetimes can determine the manifold dimension. In this work, we show that the answer to this question is in the affirmative. We investigate the properties of future or past distinguishing spacetimes and show that their causal structures determine the manifold dimension. This gives a non-trivial generalization of the MHKM theorem and suggests that there is a causal topology for FPD spacetimes which encodes manifold dimension and which is strictly finer than the Alexandrov topology. We show that such a causal topology does exist. We construct it using a convergence criterion based on sequences of 'chain intervals' which are the causal analogues of null geodesic segments. We show that when the region of strong causality violation satisfies a local achronality condition, this topology is equivalent to the manifold topology in an FPD spacetime.
Causal topology in future and past distinguishing spacetimes
Energy Technology Data Exchange (ETDEWEB)
Parrikar, Onkar [Birla Institute of Technology and Science-Pilani, Goa campus, Goa 403 726 (India); Surya, Sumati, E-mail: ssurya@rri.res.in [Raman Research Institute, CV Raman Ave, Sadashivanagar, Bangalore 560 080 (India)
2011-08-07
The causal structure of a strongly causal spacetime is particularly well endowed. Not only does it determine the conformal spacetime geometry when the spacetime dimension n > 2, as shown by Malament and Hawking-King-McCarthy (MHKM), but also the manifold dimension. The MHKM result, however, applies more generally to spacetimes satisfying the weaker causality condition of future and past distinguishability (FPD), and it is an important question whether the causal structure of such spacetimes can determine the manifold dimension. In this work, we show that the answer to this question is in the affirmative. We investigate the properties of future or past distinguishing spacetimes and show that their causal structures determine the manifold dimension. This gives a non-trivial generalization of the MHKM theorem and suggests that there is a causal topology for FPD spacetimes which encodes manifold dimension and which is strictly finer than the Alexandrov topology. We show that such a causal topology does exist. We construct it using a convergence criterion based on sequences of 'chain intervals' which are the causal analogues of null geodesic segments. We show that when the region of strong causality violation satisfies a local achronality condition, this topology is equivalent to the manifold topology in an FPD spacetime.
Hermann, Derik; Leménager, Tagrid; Gelbke, Jan; Welzel, Helga; Skopp, Gisela; Mann, Karl
2009-01-01
It is unclear whether impairment in decision making, measured by the Iowa Gambling Task (IGT), in addiction is substance-induced or the consequence of personality structure. Analysis of the IGT, the Tridimensional Personality Questionnaire (TPQ) and cannabinoids in hair and urine were performed in 13 cannabis users and matched controls. Hair Delta(9)-tetrahydrocannabinol (THC) correlated negatively with the last subtrial (cards 80-100) of the IGT (R = -0.67). In all participants (n = 26) the TPQ dimension, harm avoidance, correlated negatively with the total IGT score (R = -0.46). The last IGT-subtrial correlated with adventure seeking (R = 0.43), harm avoidance (R = -0.39) and reward dependence (R = -0.44). The last subtrial gives information on whether a participant has learned the IGT strategy. Multiple regression confirmed the impact of THC on the last subtrial, whereas TPQ personality traits did not additionally explain variance. Former indications of the IGT performance depending on the amount of cannabis consumed were replicated with an objective measurement of chronic cannabis consumption (hair THC). Multiple regression analysis argues for a stronger impact of chronic THC consumption than personality traits, but does not provide a causal relationship. Other factors (e.g. genetic) may also play a role. 2009 S. Karger AG, Basel.
Comparison of evolutionary algorithms in gene regulatory network model inference.
LENUS (Irish Health Repository)
2010-01-01
ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity
DEFF Research Database (Denmark)
Harndahl, Mikkel Nors; Rasmussen, Michael; Nielsen, Morten
2012-01-01
of antigen processing and presentation in defining cytotoxic T lymphocyte (CTL) immunogenicity Assarsson et al., 2007. Using an affinity-balanced approach, we demonstrated that immunogenic peptides tend to be more stably bound to MHC-I molecules compared with non-immunogenic peptides. We also developed......Peptide-MHC class I stability is a stronger predictor of CTL immunogenicity than peptide affinity Mikkel Harndahla, Michael Rasmussena, Morten Nielsenb, Soren Buusa,∗ a Laboratory of Experimental Immunology, Faculty of Health Sciences, University of Copenhagen, Denmark b Center for Biological...... Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Denmark Efficient presentation of peptide-MHC class I (pMHC-I) complexes to immune T cells should benefit from a stable peptide- MHC-I interaction. However, it has been difficult to distinguish stability from other...
Directory of Open Access Journals (Sweden)
Alina Galvão Spinillo
2007-01-01
Full Text Available This study, by means of using an on-line methodology, examined 7 and 9-year-old children's text comprehension in relation to different types of inferences constructed during a story reading task: causal inferences, state inferences and inferences of prediction (what happens next in the story. The on-line methodology consists of making inferential questions to the child during text comprehension immediately after the subject has read a passage. Due to the fact that inferences of prediction involve extratextual information and require to raise hypothesis about the continuity of the narrative, children had difficulties in predicting events that had not occurred yet in the story. It was concluded that the ability to make inferences during text comprehension varies according to the type of inferential question presented and that this ability develops with age. The inovative aspect of the on-line methodology and its relevance to the research on text comprehension are discussed.
Directory of Open Access Journals (Sweden)
Raman K Marwaha
2015-01-01
Full Text Available Introduction: There are conflicting reports on the relationship of lean mass (LM and fat mass (FM with bone mineral content (BMC. Given the high prevalence of Vitamin D deficiency in India, we planned the study to evaluate the relationship between LM and FM with BMC in Indian children and adolescents. The objective of the study was to evaluate the relationship of BMC with LM and FM. Materials and Methods: Total and regional BMC, LM, and FM using dual energy X-ray absorptiometry and pubertal staging were assessed in 1403 children and adolescents (boys [B]: 826; girls [G]: 577. BMC index, BMC/LM and BMC/FM ratio, were calculated. Results: The age ranged from 5 to 18 years, with a mean age of 13.2 ± 2.7 years. BMC adjusted for height (BMC index and BMC/height ratio was comparable in both genders. There was no difference in total BMC between genders in the prepubertal group but were higher in more advanced stages of pubertal maturation. The correlation of total as well as regional BMC was stronger for LM (B: Total BMC - 0.880, trunk - 0.715, leg - 0.894, arm - 0.891; G: Total BMC - 0.827, leg - 0.846, arm - 0.815 (all value indicate r2 , P < 0.0001 for all when compared with FM (B: Total BMC - 0.776, trunk - 0.676, leg - 0.772, arm - 0.728; G: Total BMC - 0.781, leg - 0.741, arm - 0.689; all P < 0.0001 except at trunk BMC (LM - 0.682 vs. FM - 0.721; all P < 0.0001, even after controlling for age, height, pubertal stage, and biochemical parameters. Conclusions: BMC had a stronger positive correlation with LM than FM.
Directory of Open Access Journals (Sweden)
Bernard N. Iyke
2014-06-01
Full Text Available This paper examines the dynamic causal relationship between electricity consumption and economic growth in Ghana within a trivariate ARDL framework, for the period 1971–2012.The paper obviates the variable omission bias, and the use of cross-sectional techniques that characterise most existing studies. The results show that there is a distinct causal flow from economic growth to electricity consumption: both in the short run and in the long run. This finding supports the growth-led electricity consumption hypothesis, as documented in the literature. The paper urges policymakers in Ghana to resort to alternative sources of electric power generation, in order to reduce any future pressures on the current sources of electricity production. Appropriate monetary policies must also be put in place, in order to accommodate potential inflation hikes stemming from excessive demands for electricity in the near future.
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.
Exploring causal associations between alcohol and coronary heart disease risk factors
DEFF Research Database (Denmark)
Lawlor, Debbie A; Nordestgaard, Børge G; Benn, Marianne
2013-01-01
pressure (BP), lipids, fibrinogen, and glucose. Analyses were undertaken in 54 604 Danes (mean age 56 years). Both confounder-adjusted multivariable and IV analyses suggested that a greater alcohol consumption among those who drank any alcohol resulted in a higher BP [mean difference in SBP per doubling...... of alcohol consumption among drinkers: 0.76 mmHg (95% CI: 0.63, 0.90) from multivariable analyses and 0.94 mmHg (-3.03, 4.69) from IV analyses; P-value for difference in these results = 0.95]. The positive association of alcohol with HDLc in the multivariable analyses [4.9% (4.7, 5.1)] appeared stronger than......AimsTo explore the causal effect of long-term alcohol consumption on coronary heart disease risk factors.Methods and resultsWe used variants in ADH1B and ADH1C genes as instrumental variables (IV) to estimate the causal effect of long-term alcohol consumption on body mass index (BMI), blood...
Computational Neuropsychology and Bayesian Inference
Directory of Open Access Journals (Sweden)
Thomas Parr
2018-02-01
Full Text Available Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine ‘prior’ beliefs with a generative (predictive model to explain the causes of sensations. Under this view, neuropsychological deficits can be thought of as false inferences that arise due to aberrant prior beliefs (that are poor fits to the real world. This draws upon the notion of a Bayes optimal pathology – optimal inference with suboptimal priors – and provides a means for computational phenotyping. In principle, any given neuropsychological disorder could be characterized by the set of prior beliefs that would make a patient’s behavior appear Bayes optimal. We start with an overview of some key theoretical constructs and use these to motivate a form of computational neuropsychology that relates anatomical structures in the brain to the computations they perform. Throughout, we draw upon computational accounts of neuropsychological syndromes. These are selected to emphasize the key features of a Bayesian approach, and the possible types of pathological prior that may be present. They range from visual neglect through hallucinations to autism. Through these illustrative examples, we review the use of Bayesian approaches to understand the link between biology and computation that is at the heart of neuropsychology.
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
SICK: THE SPECTROSCOPIC INFERENCE CRANK
International Nuclear Information System (INIS)
Casey, Andrew R.
2016-01-01
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
sick: The Spectroscopic Inference Crank
Casey, Andrew R.
2016-03-01
There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees a...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....... and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last...
Type Inference of Turbo Pascal
DEFF Research Database (Denmark)
Hougaard, Ole Ildsgaard; Schwartzbach, Michael I; Askari, Hosein
1995-01-01
Type inference is generally thought of as being an exclusive property of the functional programming paradigm. We argue that such a feature may be of significant benefit for also standard imperative languages. We present a working tool (available by WWW) providing these benefits for a full version...... of Turbo Pascal. It has the form of a preprocessor that analyzes programs in which the type annotations are only partial or even absent. The resulting program has full type annotations, will be accepted by the standard Turbo Pascal compiler, and has polymorphic use of procedures resolved by means of code...
Inferring network structure from cascades
Ghonge, Sushrut; Vural, Dervis Can
2017-07-01
Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.
Genetic Obesity and the Risk of Atrial Fibrillation – Causal Estimates from Mendelian Randomization
Chatterjee, Neal A.; Arking, Dan E.; Ellinor, Patrick T.; Heeringa, Jan; Lin, Honghuang; Lubitz, Steven A.; Soliman, Elsayed Z.; Verweij, Niek; Alonso, Alvaro; Benjamin, Emelia J.; Gudnason, Vilmundur; Stricker, Bruno H. C.; Van Der Harst, Pim; Chasman, Daniel I.; Albert, Christine M.
2017-01-01
Background Observational studies have identified an association between body mass index (BMI) and incident atrial fibrillation (AF). Inferring causality from observational studies, however, is subject to residual confounding, reverse causation, and bias. The primary objective of this study was to evaluate the causal association between BMI and AF using genetic predictors of BMI. Methods We identified 51 646 individuals of European ancestry without AF at baseline from seven prospective population-based cohorts initiated between 1987 and 2002 in the United States, Iceland, and the Netherlands with incident AF ascertained between 1987 and 2012. Cohort-specific mean follow-up ranged 7.4 to 19.2 years, over which period there were a total of 4178 cases of incident AF. We performed a Mendelian randomization with instrumental variable analysis to estimate a cohort-specific causal hazard ratio for the association between BMI and AF. Two genetic instruments for BMI were utilized: FTO genotype (rs1558902) and a BMI gene score comprised of 39 single nucleotide polymorphisms identified by genome-wide association studies to be associated with BMI. Cohort-specific estimates were combined by random-effects, inverse variance weighted meta-analysis. Results In age- and sex-adjusted meta-analysis, both genetic instruments were significantly associated with BMI (FTO: 0.43 [95% CI: 0.32 – 0.54] kg/m2 per A-allele, p<0.001); BMI gene score: 1.05 [95% CI: 0.90-1.20] kg/m2 per 1 unit increase, p<0.001) and incident AF (FTO – HR: 1.07 [1.02-1.11] per A-allele, p=0.004; BMI gene score – HR: 1.11 [1.05-1.18] per 1-unit increase, p<0.001). Age- and sex-adjusted instrumental variable estimates for the causal association between BMI and incident AF were HR 1.15 [1.04-1.26] per kg/m2, p=0.005 (FTO) and 1.11 [1.05-1.17] per kg/m2, p<0.001 (BMI gene score). Both of these estimates were consistent with the meta-analyzed estimate between observed BMI and AF (age- and sex-adjusted HR 1.05 [1
Causal Relations Drive Young Children's Induction, Naming, and Categorization
Opfer, John E.; Bulloch, Megan J.
2007-01-01
A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young…
Theories of conduct disorder: a causal modelling analysis
Krol, N.P.C.M.; Morton, J.; Bruyn, E.E.J. De
2004-01-01
Background: If a clinician has to make decisions on diagnosis and treatment, he or she is confronted with a variety of causal theories. In order to compare these theories a neutral terminology and notational system is needed. The Causal Modelling framework involving three levels of description –
Cause and Event: Supporting Causal Claims through Logistic Models
O'Connell, Ann A.; Gray, DeLeon L.
2011-01-01
Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify…
Financial networks based on Granger causality: A case study
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