A developmental approach to learning causal models for cyber security
Mugan, Jonathan
2013-05-01
To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.
What kind of causal modelling approach does personality research need?
Borsboom, D.; van der Sluis, S.; Noordhof, A.; Wichers, M.; Geschwind, N.; Aggen, S.H.; Kendler, K.S.; Cramer, A.O.J.
2012-01-01
Lee (2012) proposes that personality research should utilise recent theories of causality. Although we agree that such theories are important, we also note that their empirical application has not been very successful to date. The reason may be that psychological systems are frequently characterised
Directory of Open Access Journals (Sweden)
Tian Ge
2009-11-01
Full Text Available Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM and Granger Causal model (GCM. These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning.
Causal Analysis of Religious Violence, a Structural Equation Modeling Approach
Directory of Open Access Journals (Sweden)
M Munajat
2015-12-01
[Penelitian ini berusaha mengkaji sebab kekerasan keagamaan dengan menggunakan pendekatan Model Persamaan Struktur (SEM. Penelitian kuantitatif terdahulu dalam bidang gerakan sosial dan kekerasan politik menunjukkan bahwa setidaknya ada tiga faktor yang diduga kuat menjadi penyebab kekerasan kolektif, seperti kekerasan agama, yaitu: 1 semakin fundamentalis seseorang, maka ia akan semakin cenderung menyetujui pernggunaan cara kekerasan, 2 semakin rendah kepercayaan seseorang terhadap pemerintah, maka ia akan semakin menyetujui penggunaan kekerasan, 3 berbeda dengan pendapat ke-dua, hanya orang yang rendah kepercayaanya kepada pemerintah, namun mempunyai semangat politik tinggi, yang akan menyetujui penggunaan cara-cara kekerasan. Berdasarkan pada data yang diambil dari 343 responden dari para aktivis, Front Pembela Islam, Muhammadiyah dan Nahdlatul Ulama, penelitian ini mengkonfirmasi bahwa semakin fundamentalis seseorang, maka ia akan semakin cenderung menyetujui kekerasan, terlepas dari afiliasi organisasi mereka. Namun demikian, penelitian ini tidak mendukung hubungan antara kepercayaan terhadap pemerintah dan kekerasan. Demikian juga, hubungan antara kekerasan dan interaksi antara kepercayaan pemerintah dan semangat politik tidak dapat dibuktikan dari data dalam penelitian ini. Oleh karena itu, penelitian ini menyimpulkan bahwa fundamentalisme, sebagai salah satu bentuk keagamaan, merupakan faktor yang sangat penting dalam menjelaskan kekerasan keagamaan.
Causally nonseparable processes admitting a causal model
Feix, Adrien; Araújo, Mateus; Brukner, Časlav
2016-08-01
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.
Combining FDI and AI approaches within causal-model-based diagnosis.
Gentil, Sylviane; Montmain, Jacky; Combastel, Christophe
2004-10-01
This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.
Friston, K J; Harrison, L; Penny, W
2003-08-01
In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
2012-01-01
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by orde...
Causal diagrams for physical models
Kinsler, Paul
2015-01-01
I present a scheme of drawing causal diagrams based on physically motivated mathematical models expressed in terms of temporal differential equations. They provide a means of better understanding the processes and causal relationships contained within such systems.
A Causal, Data-driven Approach to Modeling the Kepler Data
Wang, Dun; Hogg, David W.; Foreman-Mackey, Daniel; Schölkopf, Bernhard
2016-09-01
Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science—the most precise photometric measurements of stars ever made—appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here, we present the causal pixel model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM models the systematic effects in the time series of a pixel using the pixels of many other stars and the assumption that any shared signal in these causally disconnected light curves is caused by instrumental effects. In addition, we use the target star’s future and past (autoregression). By appropriately separating, for each data point, the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the model. The method has four tuning parameters—the number of predictor stars or pixels, the autoregressive window size, and two L2-regularization amplitudes for model components, which we set by cross-validation. We determine values for tuning parameters that works well for most of the stars and apply the method to a corresponding set of target stars. We find that CPM can consistently produce low-noise light curves. In this paper, we demonstrate that pixel-level de-trending is possible while retaining transit signals, and we think that methods like CPM are generally applicable and might be useful for K2, TESS, etc., where the data are not clean postage stamps like Kepler.
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.
Causal reasoning with mental models.
Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N
2014-01-01
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.
Causal reasoning with mental models
Directory of Open Access Journals (Sweden)
Sangeet eKhemlani
2014-10-01
Full Text Available This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.
Identifiability of Causal Graphs using Functional Models
Peters, Jonas; Janzing, Dominik; Schoelkopf, Bernhard
2012-01-01
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been shown that under these assumptions the causal graph can be identified up to Markov equivalence (some arrows remain undirected) using methods like the PC algorithm. In this work we propose an alternative by defining Identifiable Functional Model Classes (IFMOCs). As our main theorem we prove that if the data generating process belongs to an IFMOC, one can identify the complete causal graph. To the best of our knowledge this is the first identifiability result of this kind that is not limited to linear functional relationships. We discuss how the IFMOC assumption and the Markov and faithfulness assumptions relate to each other and explain why we believe that the IFMOC assumption can be tested more eas...
Causal Models for Risk Management
Directory of Open Access Journals (Sweden)
Neysis Hernández Díaz
2013-12-01
Full Text Available In this work a study about the process of risk management in major schools in the world. The project management tools worldwide highlights the need to redefine risk management processes. From the information obtained it is proposed the use of causal models for risk analysis based on information from the project or company, say risks and the influence thereof on the costs, human capital and project requirements and detect the damages of a number of tasks without tribute to the development of the project. A study on the use of causal models as knowledge representation techniques causal, among which are the Fuzzy Cognitive Maps (DCM and Bayesian networks, with the most favorable MCD technique to use because it allows modeling the risk information witho ut having a knowledge base either itemize.
Identifiability of causal effect for a simple causal model
Institute of Scientific and Technical Information of China (English)
郑忠国; 张艳艳; 童行伟
2002-01-01
Counterfactual model is put forward to discuss the causal inference in the directed acyclic graph and its corresponding identifiability is thus studied with the ancillary information based on conditional independence. It is shown that the assumption of ignorability can be expanded to the assumption of replaceability,under which the causal efiects are identifiable.
Antonakis, J.
2015-01-01
Making correct causal claims is important for research and practice. This article explains what causality is, and how it can be established via experimental design. Because experiments are infeasible in many applied settings, researchers often use "observational" methods to estimate causal models. In these situations, it is likely that model estimates are compromised by endogeneity. The article discusses the conditions that engender endogeneity and methods that can eliminate it.
Modeling of causality with metamaterials
Smolyaninov, Igor I.
2013-02-01
Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space-time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space-time. While this model may be used to build interesting space-time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space-time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler-Feynman absorber theory of causality.
The metagenomic approach and causality in virology
Directory of Open Access Journals (Sweden)
Silvana Beres Castrignano
2015-01-01
Full Text Available Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease.
The metagenomic approach and causality in virology
Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico
2015-01-01
Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease. PMID:25902566
Modeling of causality with metamaterials
Smolyaninov, Igor I
2012-01-01
Hyperbolic metamaterials may be used to model a 2+1 dimensional Minkowski spacetime in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete history of this 2+1 dimensional spacetime. While this model may be used to build interesting spacetime analogs, such as metamaterial black holes and big bang, it lacks causality: since light inside the metamaterial may propagate back and force along the timelike spatial coordinate, events in the future may affect events in the past. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the timelike spatial coordinate. Resulting 2+1 Minkowski spacetime appears to be causal. Th...
Spin foam models as energetic causal sets
Cortês, Marina
2014-01-01
Energetic causal sets are causal sets endowed by a flow of energy-momentum between causally related events. These incorporate a novel mechanism for the emergence of space-time from causal relations. Here we construct a spin foam model which is also an energetic causal set model. This model is closely related to the model introduced by Wieland, and this construction makes use of results used there. What makes a spin foam model also an energetic causal set is Wieland's identification of new momenta, conserved at events (or four-simplices), whose norms are not mass, but the volume of tetrahedra. This realizes the torsion constraints, which are missing in previous spin foam models, and are needed to relate the connection dynamics to those of the metric, as in general relativity. This identification makes it possible to apply the new mechanism for the emergence of space-time to a spin foam model.
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...
Bayesian Discovery of Linear Acyclic Causal Models
Hoyer, Patrik O
2012-01-01
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accur...
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
Flux Analysis in Process Models via Causality
Kahramanoğullari, Ozan
2010-01-01
We present an approach for flux analysis in process algebra models of biological systems. We perceive flux as the flow of resources in stochastic simulations. We resort to an established correspondence between event structures, a broadly recognised model of concurrency, and state transitions of process models, seen as Petri nets. We show that we can this way extract the causal resource dependencies in simulations between individual state transitions as partial orders of events. We propose transformations on the partial orders that provide means for further analysis, and introduce a software tool, which implements these ideas. By means of an example of a published model of the Rho GTP-binding proteins, we argue that this approach can provide the substitute for flux analysis techniques on ordinary differential equation models within the stochastic setting of process algebras.
Structural equation modeling: building and evaluating causal models: Chapter 8
Grace, James B.; Scheiner, Samuel M.; Schoolmaster, Donald R.
2015-01-01
Scientists frequently wish to study hypotheses about causal relationships, rather than just statistical associations. This chapter addresses the question of how scientists might approach this ambitious task. Here we describe structural equation modeling (SEM), a general modeling framework for the study of causal hypotheses. Our goals are to (a) concisely describe the methodology, (b) illustrate its utility for investigating ecological systems, and (c) provide guidance for its application. Throughout our presentation, we rely on a study of the effects of human activities on wetland ecosystems to make our description of methodology more tangible. We begin by presenting the fundamental principles of SEM, including both its distinguishing characteristics and the requirements for modeling hypotheses about causal networks. We then illustrate SEM procedures and offer guidelines for conducting SEM analyses. Our focus in this presentation is on basic modeling objectives and core techniques. Pointers to additional modeling options are also given.
Directory of Open Access Journals (Sweden)
Arup Kumar Baksi
2012-08-01
Full Text Available Information technology induced communications (ICTs have revolutionized the operational aspects of service sector and have triggered a perceptual shift in service quality as rapid dis-intermediation has changed the access-mode of services on part of the consumers. ICT-enabled services further stimulated the perception of automated service quality with renewed dimensions and there subsequent significance to influence the behavioural outcomes of the consumers. Customer Relationship Management (CRM has emerged as an offshoot to technological breakthrough as it ensured service-encapsulation by integrating people, process and technology. This paper attempts to explore the relationship between automated service quality and its behavioural consequences in a relatively novel business-philosophy – CRM. The study has been conducted on the largest public sector bank of India - State bank of India (SBI at Kolkata which has successfully completed its decade-long operational automation in the year 2008. The study used structural equation modeling (SEM to justify the proposed model construct and causal loop diagramming (CLD to depict the negative and positive linkages between the variables.
Liang, Ling L.; Fulmer, Gavin W.; Majerich, David M.; Clevenstine, Richard; Howanski, Raymond
2012-01-01
The purpose of this study is to examine the effects of a model-based introductory physics curriculum on conceptual learning in a Physics First (PF) Initiative. This is the first comparative study in physics education that applies the Rasch modeling approach to examine the effects of a model-based curriculum program combined with PF in the United…
Testing for Causality in Variance Usinf Multivariate GARCH Models
Christian M. Hafner; Herwartz, Helmut
2008-01-01
Tests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently, little is known about their power properties. In this paper we show that a convenient alternative to residual based testing is to specify a multivariate volatility model, such as multivariate GARCH (or BEKK), and construct a Wald test on noncausality in variance. We compare both approaches to testing causality in var...
Heckman, James J.
2008-01-01
This paper presents the econometric approach to causal modeling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper ...
The Causal Foundations of Structural Equation Modeling
2012-02-16
The Causal Foundations of Structural Equation Modeling Judea Pearl University of California, Los Angeles Computer Science Department Los Angeles, CA...Handbook of Structural Equation Modeling . New York: Guilford Press. TECHNICAL REPORT R-370 February 2012 Report Documentation Page Form...COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE The Causal Foundations of Structural Equation Modeling 5a. CONTRACT NUMBER 5b. GRANT
Carbon Emissions and Economic Growth: Alternative Approaches to Causality Testing
Energy Technology Data Exchange (ETDEWEB)
Rehdanz, Katrin (Christian-Albrechts Univ., Kiel (Germany)); Maddison, David J. (Univ. of Birmingham, Dept. of Economics, Birmingham (United Kingdom))
2008-07-01
Numerous papers have examined data on energy and GDP for evidence of Granger causality. More recently this technique has been extended to looking at the relationship between carbon emissions and GDP per capita. These analyses frequently reach differing conclusions concerning the existence and direction of Granger causality. This paper compares the standard fixed-dynamic-effects approach to a heterogenous panel approach testing for evidence of a causal relationship between GDP per capita and carbon emissions per capita allowing for heterogeneity. Overall there is strong evidence for the existence of a bidirectional causal relationship between GDP per capita and CO{sub 2} emissions per capita
A Causal Model for Diagnostic Reasoning
Institute of Scientific and Technical Information of China (English)
PENG Guoqiang; CHENG Hu
2000-01-01
Up to now, there have been many methods for knowledge representation and reasoning in causal networks, but few of them include the research on the coactions of nodes. In practice, ignoring these coactions may influence the accuracy of reasoning and even give rise to incorrect reasoning. In this paper, based on multilayer causal networks, the definitions on coaction nodes are given to construct a new causal network called Coaction Causal Network, which serves to construct a model of neural network for diagnosis followed by fuzzy reasoning, and then the activation rules are given and neural computing methods are used to finish the diagnostic reasoning. These methods are proved in theory and a method of computing the number of solutions for the diagnostic reasoning is given. Finally, the experiments and the conclusions are presented.
A quantum probability model of causal reasoning.
Trueblood, Jennifer S; Busemeyer, Jerome R
2012-01-01
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.
A Quantum Probability Model of Causal Reasoning
Trueblood, Jennifer S.; Busemeyer, Jerome R.
2012-01-01
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. PMID:22593747
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.
Imposing causality on a matrix model
Energy Technology Data Exchange (ETDEWEB)
Benedetti, Dario [Perimeter Institute for Theoretical Physics, 31 Caroline St. N, N2L 2Y5, Waterloo ON (Canada)], E-mail: dbenedetti@perimeterinstitute.ca; Henson, Joe [Perimeter Institute for Theoretical Physics, 31 Caroline St. N, N2L 2Y5, Waterloo ON (Canada)
2009-07-13
We introduce a new matrix model that describes Causal Dynamical Triangulations (CDT) in two dimensions. In order to do so, we introduce a new, simpler definition of 2D CDT and show it to be equivalent to the old one. The model makes use of ideas from dually weighted matrix models, combined with multi-matrix models, and can be studied by the method of character expansion.
Ebert-Uphoff, I.; Hammerling, D.; Samarasinghe, S.; Baker, A. H.
2015-12-01
The framework of causal discovery provides algorithms that seek to identify potential cause-effect relationships from observational data. The output of such algorithms is a graph structure that indicates the potential causal connections between the observed variables. Originally developed for applications in the social sciences and economics, causal discovery has been used with great success in bioinformatics and, most recently, in climate science, primarily to identify interaction patterns between compound climate variables and to track pathways of interactions between different locations around the globe. Here we apply causal discovery to the output data of climate models to learn so-called causal signatures from the data that indicate interactions between the different atmospheric variables. These causal signatures can act like fingerprints for the underlying dynamics and thus serve a variety of diagnostic purposes. We study the use of the causal signatures for three applications: 1) For climate model software verification we suggest to use causal signatures as a means of detecting statistical differences between model runs, thus identifying potential errors and supplementing the Community Earth System Model Ensemble Consistency Testing (CESM-ECT) tool recently developed at NCAR for CESM verification. 2) In the context of data compression of model runs, we will test how much the causal signatures of the model outputs changes after different compression algorithms have been applied. This may result in additional means to determine which type and amount of compression is acceptable. 3) This is the first study applying causal discovery simultaneously to a large number of different atmospheric variables, and in the process of studying the resulting interaction patterns for the two aforementioned applications, we expect to gain some new insights into their relationships from this approach. We will present first results obtained for Applications 1 and 2 above.
A Causal Model for Fluctuating Sugar Levels in Diabetes Patients
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Kinzang Chhogyal
2012-09-01
Full Text Available Background Causal models of physiological systems can be immensely useful in medicine as they may be used for both diagnostic and therapeutic reasoning. Aims In this paper we investigate how an agent may use the theory of belief change to rectify simple causal models of changing blood sugar levels in diabetes patients. Method We employ the semantic approach to belief change together with a popular measure of distance called Dalal distance between different state descriptions in order to implement a simple application that simulates the effectiveness of the proposed method in helping an agent rectify a simple causal model. Results Our simulation results show that distance-based belief change can help in improving the agent’s causal knowledge. However, under the current implementation there is no guarantee that the agent will learn the complete model and the agent may at times get stuck in local optima. Conclusion Distance-based belief change can help in refining simple causal models such as the example in this paper. Future work will include larger state-action spaces, better distance measures and strategies for choosing actions.
Modeling of causality with metamaterials
Smolyaninov, Igor I.
2012-01-01
Hyperbolic metamaterials may be used to model a 2+1 dimensional Minkowski spacetime in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete history of this 2+1 dimensional spacetime. While this model may be used to build interesting spacetime ...
Causality in Psychiatry: A Hybrid Symptom Network Construct Model
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Gerald eYoung
2015-11-01
Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.
Causality in Psychiatry: A Hybrid Symptom Network Construct Model
Young, Gerald
2015-01-01
Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639
Testing for causality in variance using multivariate GARCH models
Hafner, Christian; Herwartz, H.
2004-01-01
textabstractTests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently little is known about their power properties. In this paper we show that a convenient alternative to residual based testing is to specify a multivariate volatility model, such as multivariate GARCH (or BEKK), and construct a Wald test on noncausality in variance. We compare both approaches to testing causa...
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…
Computation of Probabilities in Causal Models of History of Science
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Osvaldo Pessoa Jr.
2006-12-01
Full Text Available : The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations. Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY=X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY=X(t, which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances.
Hung-Pin, Lin
2014-01-01
The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries-United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.
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Lin Hung-Pin
2014-01-01
Full Text Available The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE consumption and economic growth (EG in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries—United States of America (USA, Japan, Germany, Italy, and United Kingdom (UK. The overall results indicate that (1 a short-run unidirectional causality runs from EG to RE in Italy and UK; (2 long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3 a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4 both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5 Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.
New Approaches to Establish Genetic Causality
McNally, Elizabeth M.; George, Alfred L.
2015-01-01
Cardiovascular medicine has evolved rapidly in the era of genomics with many diseases having primary genetic origins becoming the subject of intense investigation. The resulting avalanche of information on the molecular causes of these disorders has prompted a revolution in our understanding of disease mechanisms and provided new avenues for diagnoses. At the heart of this revolution is the need to correctly classify genetic variants discovered during the course of research or reported from clinical genetic testing. This review will address current concepts related to establishing the cause and effect relationship between genomic variants and heart diseases. A survey of general approaches used for functional annotation of variants will also be presented. PMID:25864169
Trivial Lagrangians in the Causal Approach
Grigore, Dan-Radu
2015-01-01
We prove the non-uniqueness theorem for the chronological products of a gauge model. We use a cohomological language where the cochains are chronological products, gauge invariance means a cocycle restriction and coboundaries are expressions producing zero sandwiched between physical states. Suppose that we have gauge invariance up to order n of the perturbation theory and we modify the first-order chronological products by a coboundary (a trivial Lagrangian). Then the chronological products up to order n get modified by a coboundary also.
Energy Technology Data Exchange (ETDEWEB)
Shahbaz, Muhammad [COMSATS Institute of Information Technology, Lahore (Pakistan); Tang, Chor Foon, E-mail: tcfoon@yahoo.com [Department of Economics, Faculty of Economics and Administration, University of Malaya, 50603 Kuala Lumpur (Malaysia); Shahbaz Shabbir, Muhammad [University of Illinois at Urbana-Champaign, Champaign (United States)
2011-06-15
The aim of this paper is to re-examine the relationship between electricity consumption, economic growth, and employment in Portugal using the cointegration and Granger causality frameworks. This study covers the sample period from 1971 to 2009. We examine the presence of a long-run equilibrium relationship using the bounds testing approach to cointegration within the Unrestricted Error-Correction Model (UECM). Moreover, we examine the direction of causality between electricity consumption, economic growth, and employment in Portugal using the Granger causality test within the Vector Error-Correction Model (VECM). As a summary of the empirical findings, we find that electricity consumption, economic growth, and employment in Portugal are cointegrated and there is bi-directional Granger causality between the three variables in the long-run. With the exception of the Granger causality between electricity consumption and economic growth, the rest of the variables are also bi-directional Granger causality in the short-run. Furthermore, we find that there is unidirectional Granger causality running from economic growth to electricity consumption, but no evidence of reversal causality. - Highlights: > We re-examine the relationship between electricity consumption, economic growth, and employment in Portugal. > The electricity consumption and economic growth is causing each other in the long-run. > In the short-run, economic growth Granger-cause electricity consumption, but no evidence of reversal causality. > Energy conservation policy will deteriorate the process of economic growth in the long-run. > Portugal should increase investment on R and D to design new energy savings technology.
Spectral dimension in graph models of causal quantum gravity
Giasemidis, Georgios
2013-01-01
The phenomenon of scale dependent spectral dimension has attracted special interest in the quantum gravity community over the last eight years. It was first observed in computer simulations of the causal dynamical triangulation (CDT) approach to quantum gravity and refers to the reduction of the spectral dimension from 4 at classical scales to 2 at short distances. Thereafter several authors confirmed a similar result from different approaches to quantum gravity. Despite the contribution from different approaches, no analytical model was proposed to explain the numerical results as the continuum limit of CDT. In this thesis we introduce graph ensembles as toy models of CDT and show that both the continuum limit and a scale dependent spectral dimension can be defined rigorously. First we focus on a simple graph ensemble, the random comb. It does not have any dynamics from the gravity point of view, but serves as an instructive toy model to introduce the characteristic scale of the graph, study the continuum li...
Causal structure and hierarchies of models.
Hoover, Kevin D
2012-12-01
Economics prefers complete explanations: general over partial equilibrium, microfoundational over aggregate. Similarly, probabilistic accounts of causation frequently prefer greater detail to less as in typical resolutions of Simpson's paradox. Strategies of causal refinement equally aim to distinguish direct from indirect causes. Yet, there are countervailing practices in economics. Representative-agent models aim to capture economic motivation but not to reduce the level of aggregation. Small structural vector-autoregression and dynamic stochastic general-equilibrium models are practically preferred to larger ones. The distinction between exogenous and endogenous variables suggests partitioning the world into distinct subsystems. The tension in these practices is addressed within a structural account of causation inspired by the work of Herbert Simon's, which defines cause with reference to complete systems adapted to deal with incomplete systems and piecemeal evidence. The focus is on understanding the constraints that a structural account of causation places on the freedom to model complex or lower-order systems as simpler or higher-order systems and on to what degree piecemeal evidence can be incorporated into a structural account.
Causal Information Approach to Partial Conditioning in Multivariate Data Sets
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D. Marinazzo
2012-01-01
Full Text Available When evaluating causal influence from one time series to another in a multivariate data set it is necessary to take into account the conditioning effect of the other variables. In the presence of many variables and possibly of a reduced number of samples, full conditioning can lead to computational and numerical problems. In this paper, we address the problem of partial conditioning to a limited subset of variables, in the framework of information theory. The proposed approach is tested on simulated data sets and on an example of intracranial EEG recording from an epileptic subject. We show that, in many instances, conditioning on a small number of variables, chosen as the most informative ones for the driver node, leads to results very close to those obtained with a fully multivariate analysis and even better in the presence of a small number of samples. This is particularly relevant when the pattern of causalities is sparse.
Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy
von Eye, Alexander; Wiedermann, Wolfgang
2015-01-01
Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…
A systematic approach to multifactorial cardiovascular disease: causal analysis.
Schwartz, Stephen M; Schwartz, Hillel T; Horvath, Steven; Schadt, Eric; Lee, Su-In
2012-12-01
The combination of systems biology and large data sets offers new approaches to the study of cardiovascular diseases. These new approaches are especially important for the common cardiovascular diseases that have long been described as multifactorial. This promise is undermined by biologists' skepticism of the spider web-like network diagrams required to analyze these large data sets. Although these spider webs resemble composites of the familiar biochemical pathway diagrams, the complexity of the webs is overwhelming. As a result, biologists collaborate with data analysts whose mathematical methods seem much like those of experts using Ouija boards. To make matters worse, it is not evident how to design experiments when the network implies that many molecules must be part of the disease process. Our goal is to remove some of this mystery and suggest a simple experimental approach to the design of experiments appropriate for such analysis. We will attempt to explain how combinations of data sets that include all possible variables, graphical diagrams, complementation of different data sets, and Bayesian analyses now make it possible to determine the causes of multifactorial cardiovascular disease. We will describe this approach using the term causal analysis. Finally, we will describe how causal analysis is already being used to decipher the interactions among cytokines as causes of cardiovascular disease.
A Causal Model of Consumer-Based Brand Equity
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Szőcs Attila
2015-12-01
Full Text Available Branding literature suggests that consumer-based brand equity (CBBE is a multidimensional construct. Starting from this approach and developing a conceptual multidimensional model, this study finds that CBBE can be best modelled with a two-dimensional structure and claims that it achieves this result by choosing the theoretically based causal specification. On the contrary, with reflective specification, one will be able to fit almost any valid construct because of the halo effect and common method bias. In the final model, Trust (in quality and Advantage are causing the second-order Brand Equity. The two-dimensional brand equity is an intuitive model easy to interpret and easy to measure, which thus may be a much more attractive means for the management as well.
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 ...
The stochastic system approach to causality with a view toward lifecourse epidemiology
Commenges, Daniel
2012-01-01
The approach of causality based on physical laws and systems is revisited. The issue of "levels", the relevance to epidemiology and the definition of effects are particularly developed. Moreover it is argued that this approach that we call the stochastic system approach is particularly well fitted to study lifecourse epidemiology. A hierarchy of factors is described that could be modeled using a suitable multivariate stochastic process. To illustrate this approach, a conceptual model for coronary heart disease mixing continuous and discrete state-space processes is proposed.
Interactions between causal models, theories, and social cognitive development.
Sobel, David M; Buchanan, David W; Butterfield, Jesse; Jenkins, Odest Chadwicke
2010-01-01
We propose a model of social cognitive development based not on a single modeling framework or the hypothesis that a single model accounts for children's developing social cognition. Rather, we advocate a Causal Model approach (cf. Waldmann, 1996), in which models of social cognitive development take the same position as theories of social cognitive development, in that they generate novel empirical hypotheses. We describe this approach and present three examples across various aspects of social cognitive development. Our first example focuses on children's understanding of pretense and involves only considering assumptions made by a computational framework. The second example focuses on children's learning from "testimony". It uses a modeling framework based on Markov random fields as a computational description of a set of empirical phenomena, and then tests a prediction of that description. The third example considers infants' generalization of action learned from imitation. Here, we use a modified version of the Rational Model of Categorization to explain children's inferences. Taken together, these examples suggest that research in social cognitive development can be assisted by considering how computational modeling can lead researchers towards testing novel hypotheses.
Causal Bayes Model of Mathematical Competence in Kindergarten
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Božidar Tepeš
2016-06-01
Full Text Available In this paper authors define mathematical competences in the kindergarten. The basic objective was to measure the mathematical competences or mathematical knowledge, skills and abilities in mathematical education. Mathematical competences were grouped in the following areas: Arithmetic and Geometry. Statistical set consisted of 59 children, 65 to 85 months of age, from the Kindergarten Milan Sachs from Zagreb. The authors describe 13 variables for measuring mathematical competences. Five measuring variables were described for the geometry, and eight measuring variables for the arithmetic. Measuring variables are tasks which children solved with the evaluated results. By measuring mathematical competences the authors make causal Bayes model using free software Tetrad 5.2.1-3. Software makes many causal Bayes models and authors as experts chose the model of the mathematical competences in the kindergarten. Causal Bayes model describes five levels for mathematical competences. At the end of the modeling authors use Bayes estimator. In the results, authors describe by causal Bayes model of mathematical competences, causal effect mathematical competences or how intervention on some competences cause other competences. Authors measure mathematical competences with their expectation as random variables. When expectation of competences was greater, competences improved. Mathematical competences can be improved with intervention on causal competences. Levels of mathematical competences and the result of intervention on mathematical competences can help mathematical teachers.
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.
Qualitative analysis of causal cosmological models
Triginer, J
1996-01-01
The Einstein's field equations of Friedmann-Robertson-Walker universes filled with a dissipative fluid described by both the {\\em truncated} and {\\em non-truncated} causal transport equations are analyzed using techniques from dynamical systems theory. The equations of state, as well as the phase space, are different from those used in the recent literature. In the de Sitter expansion both the hydrodynamic approximation and the non-thermalizing condition can be fulfilled simultaneously. For \\Lambda=0 these expansions turn out to be stable provided a certain parameter of the fluid is lower than 1/2. The more general case \\Lambda>0 is studied in detail as well.
Toward an integrated, causal, and psychological model of climato-economics.
Loughnan, Steve; Bratanova, Boyka; Kuppens, Peter
2013-10-01
Van de Vliert puts forward a model of how climate and economics interact to shape human needs, stresses, and freedoms. Although we applaud the construction of this model, we suggest that more needs to be done. Specifically, by adopting a multi-level and experimental approach, we can develop an integrated, causal, and psychological model of climato-economics.
Linkage intensity learning approach with genetic algorithm for causality diagram
Institute of Scientific and Technical Information of China (English)
WANG Cheng-liang; CHEN Juan-juan
2007-01-01
The causality diagram theory, which adopts graphical expression of knowledge and direct intensity of causality, overcomes some shortages in belief network and has evolved into a mixed causality diagram methodology for discrete and continuous variable. But to give linkage intensity of causality diagram is difficult, particularly in many working conditions in which sampling data are limited or noisy. The classic learning algorithm is hard to be adopted. We used genetic algorithm to learn linkage intensity from limited data. The simulation results demonstrate that this algorithm is more suitable than the classic algorithm in the condition of sample shortage such as space shuttle's fault diagnoisis.
Valente, Bruno D.; Morota, Gota; Peñagaricano, Francisco; Gianola, Daniel; Weigel, Kent; Rosa, Guilherme J. M.
2015-01-01
The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability. PMID:25908318
Causal Analysis for Performance Modeling of Computer Programs
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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.
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
A new causal model of dental diseases associated with endocarditis.
Drangsholt, M T
1998-07-01
Infective endocarditis (IE) is a serious disease that is associated with dental diseases and treatment. The objective of this study was to summarize the epidemiological information about IE and reevaluate previous causal models in light of this evidence. The world biomedical literature was searched from 1930 to 1996 for descriptive and analytic epidemiological studies of IE. Multiple searching strategies were performed on 9 databases, including MEDLINE, CATLINE, and WORLDCAT. Results show that: 1) the incidence of IE varies between 0.70 to 6.8 per 100,000 person-years: 2) the incidence of IE increases 20 fold with advancing age: 3) over 50% of all IE cases are not associated with either an obvious procedural or infectious event 3 months prior to developing symptoms; 4) about 8% of all IE cases are associated with periodontal or dental disease without a dental procedure: 5) the time from the diagnosis of heart valve deformities to the development of IE approaches 20 years: 6) the median time from identifiable procedures to the onset of IE symptoms is about 2 to 4 weeks: 7) the risk of IE after a dental procedure is probably in the range of 1 per 3,000 to 5,000 procedures: and 8) over 80% of all IE cases are acquired in the community, and the bacteria are part of the host's endogenous flora. The synthesis of these data demonstrates that IE is a disorder with the epidemiological picture of a chronic disease such as cancer, instead of an acute infectious disease, with a long latent period and possibly several definable intermediates or stages. A new causal model is proposed that includes early bacteremias that may "prime" the endothelial surface of the heart valves over many years, and a late bacteremia over days to weeks that allows adherence and colonization of the valve, resulting in the characteristic fulminant infection.
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
Causal Model of Stress and Coping: Women in Management.
Long, Bonita C.; And Others
1992-01-01
Tested model of managerial women's (n=249) stress. Model was developed from Lazarus's theoretical framework of stress/coping and incorporated causal antecedent constructs (demographics, sex role attitudes, agentic traits), mediating constructs (environment, appraisals, engagement coping, disengagement coping), and outcomes (work performance,…
The Role of Causal Models in Analogical Inference
Lee, Hee Seung; Holyoak, Keith J.
2008-01-01
Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs and in particular on shared higher order relations. In contrast, work in philosophy of science suggests that analogical inference is also guided by causal models of the source and target. In 3…
A Causal Model of Teacher Acceptance of Technology
Chang, Jui-Ling; Lieu, Pang-Tien; Liang, Jung-Hui; Liu, Hsiang-Te; Wong, Seng-lee
2012-01-01
This study proposes a causal model for investigating teacher acceptance of technology. We received 258 effective replies from teachers at public and private universities in Taiwan. A questionnaire survey was utilized to test the proposed model. The Lisrel was applied to test the proposed hypotheses. The result shows that computer self-efficacy has…
Ortega, Pedro A
2011-01-01
Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans use strong prior knowledge; and rather than encoding hard causal relationships, they encode beliefs over causal structures, allowing for sound generalization from the observations they obtain from directly acting in the world. In this work we propose a Bayesian approach to causal induction which allows modeling beliefs over multiple causal hypotheses and predicting the behavior of the world under causal interventions. We then illustrate how this method extracts causal information from data containing interventions and observations.
Dynamic causal modelling of brain-behaviour relationships.
Rigoux, L; Daunizeau, J
2015-08-15
In this work, we expose a mathematical treatment of brain-behaviour relationships, which we coin behavioural Dynamic Causal Modelling or bDCM. This approach aims at decomposing the brain's transformation of stimuli into behavioural outcomes, in terms of the relative contribution of brain regions and their connections. In brief, bDCM places the brain at the interplay between stimulus and behaviour: behavioural outcomes arise from coordinated activity in (hidden) neural networks, whose dynamics are driven by experimental inputs. Estimating neural parameters that control network connectivity and plasticity effectively performs a neurobiologically-constrained approximation to the brain's input-outcome transform. In other words, neuroimaging data essentially serves to enforce the realism of bDCM's decomposition of input-output relationships. In addition, post-hoc artificial lesions analyses allow us to predict induced behavioural deficits and quantify the importance of network features for funnelling input-output relationships. This is important, because this enables one to bridge the gap with neuropsychological studies of brain-damaged patients. We demonstrate the face validity of the approach using Monte-Carlo simulations, and its predictive validity using empirical fMRI/behavioural data from an inhibitory control task. Lastly, we discuss promising applications of this work, including the assessment of functional degeneracy (in the healthy brain) and the prediction of functional recovery after lesions (in neurological patients).
Testing for causality in variance using multivariate GARCH models
C.M. Hafner (Christian); H. Herwartz
2004-01-01
textabstractTests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently little is known about their power properties. In this paper we show that a convenient alternative to residual
Political Socialization and Mass Media Use: A Reverse Causality Model.
Tan, Alexis S.
A reverse causality model treating mass media use for public affairs information as a result rather than as a cause of political behavior was tested utilizing surveys of 190 Mexican-American, 176 black, and 225 white adults. The criterion variable used in each sample was frequency of television and newspaper use for public affairs information. The…
Greenland, Sander; Mansournia, Mohammad Ali
2015-10-01
We describe how ordinary interpretations of causal models and causal graphs fail to capture important distinctions among ignorable allocation mechanisms for subject selection or allocation. We illustrate these limitations in the case of random confounding and designs that prevent such confounding. In many experimental designs individual treatment allocations are dependent, and explicit population models are needed to show this dependency. In particular, certain designs impose unfaithful covariate-treatment distributions to prevent random confounding, yet ordinary causal graphs cannot discriminate between these unconfounded designs and confounded studies. Causal models for populations are better suited for displaying these phenomena than are individual-level models, because they allow representation of allocation dependencies as well as outcome dependencies across individuals. Nonetheless, even with this extension, ordinary graphical models still fail to capture distinctions between hypothetical superpopulations (sampling distributions) and observed populations (actual distributions), although potential-outcome models can be adapted to show these distinctions and their consequences.
A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes.
Zhou, Jincheng; Chu, Haitao; Hudgens, Michael G; Halloran, M Elizabeth
2016-01-15
To estimate causal effects of vaccine on post-infection outcomes, Hudgens and Halloran (2006) defined a post-infection causal vaccine efficacy estimand VEI based on the principal stratification framework. They also derived closed forms for the maximum likelihood estimators of the causal estimand under some assumptions. Extending their research, we propose a Bayesian approach to estimating the causal vaccine effects on binary post-infection outcomes. The identifiability of the causal vaccine effect VEI is discussed under different assumptions on selection bias. The performance of the proposed Bayesian method is compared with the maximum likelihood method through simulation studies and two case studies - a clinical trial of a rotavirus vaccine candidate and a field study of pertussis vaccination. For both case studies, the Bayesian approach provided similar inference as the frequentist analysis. However, simulation studies with small sample sizes suggest that the Bayesian approach provides smaller bias and shorter confidence interval length.
Dark matter perturbations and viscosity: a causal approach
Acquaviva, Giovanni; Pénin, Aurélie
2016-01-01
The inclusion of dissipative effects in cosmic fluids modifies their clustering properties and could have observable effects on the formation of large scale structures. We analyse the evolution of density perturbations of cold dark matter endowed with causal bulk viscosity. The perturbative analysis is carried out in the Newtonian approximation and the bulk viscosity is described by the causal Israel-Stewart (IS) theory. In contrast to the non-causal Eckart theory, we obtain a third order evolution equation for the density contrast that depends on three free parameters. For certain parameter values, the density contrast and growth factor in IS mimic their behaviour in $\\Lambda$CDM when $z \\geq 1$. Interestingly, and contrary to intuition, certain sets of parameters lead to an increase of the clustering.
Causality in 1+1-dimensional Yukawa model-II
Indian Academy of Sciences (India)
Asrarul Haque; Satish D Joglekar
2013-10-01
The limits → large, $M →$ large with ($g^{3}/M$) = const. of the 1+1-dimensional Yukawa model are discussed. The conclusion of the results on bound states of the Yukawa model in this limit (obtained in arXiv:0908.4510v3 [hep-th]) is taken into account. It is found that model reduces to an effective non-local 3 theory in this limit. Causality violation also is observed in this limit.
Chain graph models and their causal interpretations
DEFF Research Database (Denmark)
Lauritzen, Steffen Lilholt; Richardson, Thomas S.
2002-01-01
the equilibrium distributions of dynamic models with feed-back. These dynamic interpretations lead to a simple theory of intervention, extending the theory developed for directed acyclic graphs. Finally, we contrast chain graph models under this interpretation with simultaneous equation models which have......Chain graphs are a natural generalization of directed acyclic graphs and undirected graphs. However, the apparent simplicity of chain graphs belies the subtlety of the conditional independence hypotheses that they represent. There are many simple and apparently plausible, but ultimately fallacious......, interpretations of chain graphs that are often invoked, implicitly or explicitly. These interpretations also lead to flawed methods for applying background knowledge to model selection. We present a valid interpretation by showing how the distribution corresponding to a chain graph may be generated from...
Causal Models for Safety Assurance Technologies Project
National Aeronautics and Space Administration — Fulfillment of NASA's System-Wide Safety and Assurance Technology (SSAT) project at NASA requires leveraging vast amounts of data into actionable knowledge. Models...
Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia
2015-01-01
With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com
Measuring causality by taking the directional symbolic mutual information approach
Institute of Scientific and Technical Information of China (English)
Chen Gui; Xie Lei; Chu Jian
2013-01-01
We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other.This provides a new perspective that is different from the conventional conceptions.Based on this point of view,a new causality index is derived that uses the definition of directional symbolic mutual information.This measure presents properties that are different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series.In addition to characterizing the direction and the amplitude of the information flow,it can also detect coupling delays.This method has the property of robustness,conceptual simplicity,and fast computational speed.
Causality between regional stock markets: A frequency domain approach
Directory of Open Access Journals (Sweden)
Gradojević Nikola
2013-01-01
Full Text Available Using a data set from five regional stock exchanges (Serbia, Croatia, Slovenia, Hungary and Germany, this paper presents a frequency domain analysis of a causal relationship between the returns on the CROBEX, SBITOP, CETOP and DAX indices, and the return on the major Serbian stock exchange index, BELEX 15. We find evidence of a somewhat dominant effect of the CROBEX and CETOP stock indices on the BELEX 15 stock index across a range of frequencies. The results also indicate that the BELEX 15 index and the SBITOP index interact in a bi-directional causal fashion. Finally, the DAX index movements consistently drive the BELEX 15 index returns for cycle lengths between 3 and 11 days without any feedback effect.
Scientific realism in particle physics a causal approach
Egg, Matthias
2014-01-01
Does particle physics really describe the basic constituents of the material world or is it just a useful tool for deriving empirical predictions? This book proposes a novel answer to that question, emphasizing the importance of causal reasoning for the justification of scientific claims. It thereby responds to general worries about scientific realism as well as to more specific challenges stemming from the interpretation of quantum physics.
On the Identifiability of the Post-Nonlinear Causal Model
Zhang, Kun
2012-01-01
By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal model has demonstrated its excellent performance in distinguishing the cause from effect. However, its identifiability has not been properly addressed, and how to apply it in the case of more than two variables is also a problem. In this paper, we conduct a systematic investigation on its identifiability in the two-variable case. We show that this model is identifiable in most cases; by enumerating all possible situations in which the model is not identifiable, we provide sufficient conditions for its identifiability. Simulations are given to support the theoretical results. Moreover, in the case of more than two variables, we show that the whole causal structure can be found by applying the PNL causal model to each structure in the Markov equivalent class and testing if the disturbance is independent of the direct causes for each va...
Teaching-Learning by Means of a Fuzzy-Causal User Model
Peña Ayala, Alejandro
In this research the teaching-learning phenomenon that occurs during an E-learning experience is tackled from a fuzzy-causal perspective. The approach is suitable for dealing with intangible objects of a domain, such as personality, that are stated as linguistic variables. In addition, the bias that teaching content exerts on the user’s mind is sketched through causal relationships. Moreover, by means of fuzzy-causal inference, the user’s apprenticeship is estimated prior to delivering a lecture. This supposition is taken into account to adapt the behavior of a Web-based education system (WBES). As a result of an experimental trial, volunteers that took options of lectures chosen by this user model (UM) achieved higher learning than participants who received lectures’ options that were randomly selected. Such empirical evidence contributes to encourage researchers of the added value that a UM offers to adapt a WBES.
Do trend extraction approaches affect causality detection in climate change studies?
Huang, Xu; Hassani, Hossein; Ghodsi, Mansi; Mukherjee, Zinnia; Gupta, Rangan
2017-03-01
Various scientific studies have investigated the causal link between solar activity (SS) and the earth's temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM) - an advanced non-parametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.
Research on power grid loss prediction model based on Granger causality property of time series
Energy Technology Data Exchange (ETDEWEB)
Wang, J. [North China Electric Power Univ., Beijing (China); State Grid Corp., Beijing (China); Yan, W.P.; Yuan, J. [North China Electric Power Univ., Beijing (China); Xu, H.M.; Wang, X.L. [State Grid Information and Telecommunications Corp., Beijing (China)
2009-03-11
This paper described a method of predicting power transmission line losses using the Granger causality property of time series. The stable property of the time series was investigated using unit root tests. The Granger causality relationship between line losses and other variables was then determined. Granger-caused time series were then used to create the following 3 prediction models: (1) a model based on line loss binomials that used electricity sales to predict variables, (2) a model that considered both power sales and grid capacity, and (3) a model based on autoregressive distributed lag (ARDL) approaches that incorporated both power sales and the square of power sales as variables. A case study of data from China's electric power grid between 1980 and 2008 was used to evaluate model performance. Results of the study showed that the model error rates ranged between 2.7 and 3.9 percent. 6 refs., 3 tabs., 1 fig.
DEFF Research Database (Denmark)
Kogelman, Lisette; Zhernakova, Daria V.; Westra, Harm-Jan
2015-01-01
BACKGROUND: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about...... the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from...... a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. METHODS: Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential...
An Isometric Dynamics for a Causal Set Approach to Discrete Quantum Gravity
Gudder, Stan
2014-01-01
We consider a covariant causal set approach to discrete quantum gravity. We first review the microscopic picture of this approach. In this picture a universe grows one element at a time and its geometry is determined by a sequence of integers called the shell sequence. We next present the macroscopic picture which is described by a sequential growth process. We introduce a model in which the dynamics is governed by a quantum transition amplitude. The amplitude satisfies a stochastic and unitary condition and the resulting dynamics becomes isometric. We show that the dynamics preserves stochastic states. By "doubling down" on the dynamics we obtain a unitary group representation and a natural energy operator. These unitary operators are employed to define canonical position and momentum operators.
Emergence of Four Dimensions in the Causal Set Approach to Discrete Quantum Gravity
Gudder, Stan
2015-01-01
One could begin a study like the present one by simply postulating that our universe is four-dimensional. There are ample reasons for doing this. Experience, observation and experiment all point to the fact that we inhabit a four-dimensional universe. Another approach would be to show that four-dimensions arise naturally from a reasonable model of the universe or multiverse. After reviewing the causal set approach to discrete quantum gravity in Section~1, we shall discuss the emergence of four-dimensions in Section~2. We shall see that certain patterns of four arise that suggest the introduction of a 4-dimensional discrete manifold. In the later sections we shall discuss some consequences of this introduced framework. In particular, we will show that quantum amplitudes can be employed to describe a multiverse dynamics. Moreover, a natural unitary operator together with energy, position and momentum operators will be introduced and their properties studied.
Transfer effects between moral dilemmas: a causal model theory.
Wiegmann, Alex; Waldmann, Michael R
2014-04-01
Evaluations of analogous situations are an important source for our moral intuitions. A puzzling recent set of findings in experiments exploring transfer effects between intuitions about moral dilemmas has demonstrated a striking asymmetry. Transfer often occurred with a specific ordering of moral dilemmas, but not when the sequence was reversed. In this article we present a new theory of transfer between moral intuitions that focuses on two components of moral dilemmas, namely their causal structure and their default evaluations. According to this theory, transfer effects are expected when the causal models underlying the considered dilemmas allow for a mapping of the highlighted aspect of the first scenario onto the causal structure of the second dilemma, and when the default evaluations of the two dilemmas substantially differ. The theory's key predictions for the occurrence and the direction of transfer effects between two moral dilemmas are tested in five experiments with various variants of moral dilemmas from different domains. A sixth experiment tests the predictions of the theory for how the target action in the moral dilemmas is represented.
Causal Dynamical Triangulation of 3D Tensor Model
Kawabe, Hiroshi
2016-01-01
We extend the string field theory of the two dimensional (2D) generalized causal dynamical triangulation (GCDT) with the Ishibashi-Kawai (IK-) type interaction formulated by the matrix model, to the three dimensional (3D) model of the surface field theory. Based on the loop gas model, we construct a tensor model for the discretized surface field and then apply it the stochastic quantization method. In the double scaling limit, the model is characterized by two scaling dimensions $D$ and $D_N$, the power indices of the minimal length as the scaling parameter. The continuum GCDT model with the IK-type interaction is realized with the similar restriction in the $D_N$-$D$ space, to the 2D model. The distinct property in the 3D model is that the quantum effect contains the IK-type interaction only, while the ordinary splitting interaction is excluded.
Inflation and Dirac in the Causal Set Approach to Discrete Quantum Gravity
Gudder, Stan
2015-01-01
In this approach to discrete quantum gravity the basic structural element is a covariant causal set ($c$-causet). The geometry of a $c$-causet is described by a shell-sequence that determines the discrete gravity of a universe. In this growth model, universes evolve in discrete time by adding new vertices to their generating $c$-causet. We first describe an inflationary period that is common to all universes. After this very brief cycle, the model enters a multiverse period in which the system diverges in various ways forming paths of $c$-causets. At the beginning of the multiverse period, the structure of a four-dimensional discrete manifold emerges and quantum mechanics enters the picture. A natural Hilbert space is defined and a discrete, free Dirac operator is introduced. We determine the eigenvalues and eigenvectors of this operator. Finally, we propose values for coupling constants that determine multiverse probabilities. These probabilities predict the dominance of pulsating universes.
Energy Technology Data Exchange (ETDEWEB)
Salazar-Ferrer, P.
1995-06-01
In complex industrial process control, causal reasoning appears as a major component in operators` cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs.
Risk-Based Causal Modeling of Airborne Loss of Separation
Geuther, Steven C.; Shih, Ann T.
2015-01-01
Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.
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.
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data
2014-11-01
queries of the form P (y|do(x)). We show that causal queries may be recoverable even when the factors in their identifying estimands are not...well as causal queries of the form P(yjdo(x)). We show that causal queries may be recoverable even when the factors in their identifying estimands are...Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan and Judea Pearl Cognitive Systems Laboratory
Richards, P.
2011-01-01
Many accounts of cultural factors in armed conflicts are dependent on circumstantial details. Alternative quantitative approaches suffer from confusion of correlation and cause. This paper describes and exemplifies a third approach to the analysis of cultural factors in war—causal process tracing. S
Spatiotemporal causal modeling for the management of Dengue Fever
Yu, Hwa-Lung; Huang, Tailin; Lee, Chieh-Han
2015-04-01
Increasing climatic extremes have caused growing concerns about the health effects and disease outbreaks. The association between climate variation and the occurrence of epidemic diseases play an important role on a country's public health systems. Part of the impacts are direct casualties associated with the increasing frequency and intensity of typhoons, the proliferation of disease vectors and the short-term increase of clinic visits on gastro-intestinal discomforts, diarrhea, dermatosis, or psychological trauma. Other impacts come indirectly from the influence of disasters on the ecological and socio-economic systems, including the changes of air/water quality, living environment and employment condition. Previous risk assessment studies on dengue fever focus mostly on climatic and non-climatic factors and their association with vectors' reproducing pattern. The public-health implication may appear simple. Considering the seasonal changes and regional differences, however, the causality of the impacts is full of uncertainties. Without further investigation, the underlying dengue fever risk dynamics may not be assessed accurately. The objective of this study is to develop an epistemic framework for assessing dynamic dengue fever risk across space and time. The proposed framework integrates cross-departmental data, including public-health databases, precipitation data over time and various socio-economic data. We explore public-health issues induced by typhoon through literature review and spatiotemporal analytic techniques on public health databases. From those data, we identify relevant variables and possible causal relationships, and their spatiotemporal patterns derived from our proposed spatiotemporal techniques. Eventually, we create a spatiotemporal causal network and a framework for modeling dynamic dengue fever risk.
Goal orientations in sport: a causal model Orientaciones de Meta en el deporte: un modelo causal
Directory of Open Access Journals (Sweden)
Francisco P. Holgado
2010-05-01
Full Text Available The study is based on research work relating goal orientation in sport with contextual variables and personal variables. The sample was 511 professional athletes. A “causal” model is proposed in which task and goal ego orientations are the dependent variables. A hypothetical model is obtained using structural equations modelling, supporting that: a athletes who find satisfaction experimenting mastery, who perceive a motivational climate that rewards hard work and who believe that success depends on their effort, develop task goal orientation; and b athletes who get satisfaction demonstrating greater capacity than the rest, who live a motivational climate that leads them to be better than the others and that only rewards the best players, and whose main motive for practising sport is to achieve certain social status and popularity, will have an ego goal orientation. Este trabajo parte de las investigaciones que relacionan las orientaciones de meta en el deporte con variables contextuales, como el clima motivacional percibido, y con variables personales, tales como la satisfacción con los resultados deportivos, las creencias relacionadas con los factores implicados en la obtención del éxito y los motivos por lo que se practica deporte. La muestra está compuesta por 511 deportistas profesionales. Se llevan a cabo análisis de regresión múltiple y se propone un modelo causal en el que las variables a predecir son las orientaciones de meta, a la tarea y al ego. Con ecuaciones estructurales se contrasta un modelo hipotético, que presenta un ajuste adecuado, y que defiende que: a el deportista que encuentra la satisfacción experimentando maestría, que percibe un clima motivacional que premia el trabajo duro y que cree que el éxito depende de su esfuerzo, desarrolla una orientación de meta a la tarea: y b que el deportista que obtiene satisfacción demostrando mayor capacidad que los demás, que vive un clima motivacional que le conduce a
Siggiridou, Elsa; Kugiumtzis, Dimitris
2016-04-01
Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different time series lengths. For nonlinear systems, CGCI from the restricted VAR representations are compared with analogous nonlinear causality indices. Further, CGCI in conjunction with BTS and other restricted VAR representations is applied to multi-channel scalp electroencephalogram (EEG) recordings of epileptic patients containing epileptiform discharges. CGCI on the restricted VAR, and BTS in particular, could track the changes in brain connectivity before, during and after epileptiform discharges, which was not possible using the full VAR representation.
DEFF Research Database (Denmark)
Husemoen, L. L. N.; Skaaby, T.; Martinussen, Torben;
2014-01-01
Background/Objectives: The aim was to examine the causal effect of vitamin D on serum adiponectin using a multiple instrument Mendelian randomization approach. Subjects/Methods: Serum 25-hydroxy vitamin D (25(OH)D) and serum total or high molecular weight (HMW) adiponectin were measured in two Da...... a causal relationship.......Background/Objectives: The aim was to examine the causal effect of vitamin D on serum adiponectin using a multiple instrument Mendelian randomization approach. Subjects/Methods: Serum 25-hydroxy vitamin D (25(OH)D) and serum total or high molecular weight (HMW) adiponectin were measured in two...... doubling of 25(OH)D was 4.78, 95% CI: 1.96, 7.68, Pcausal effect in % was estimated to 61.46, 95% CI: 17.51, 120.28, P=0.003 higher adiponectin per doubling of 25(OH)D. In the MONICA10...
Causal Inference and Model Selection in Complex Settings
Zhao, Shandong
Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly
Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.
Jaeger, Savina; Min, Junxia; Nigsch, Florian; Camargo, Miguel; Hutz, Janna; Cornett, Allen; Cleaver, Stephen; Buckler, Alan; Jenkins, Jeremy L
2014-06-01
Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.
Reininghaus, Ulrich; Depp, Colin A; Myin-Germeys, Inez
2016-03-01
Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals' daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions.
Shadish, William R.
2010-01-01
This article compares Donald Campbell's and Donald Rubin's work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell's work…
Directory of Open Access Journals (Sweden)
Blossfeld, Hans-Peter
2001-01-01
Full Text Available FrenchOne of the most important advances brought about by life course and eventhistory studies is the use of parallel or independent processes as explaining history factors intransition rate models. The purpose of this paper is to demonstrate a causal approach to the study ofinterrelated family events. Various types of interdependent processes are described first, followed bytwo event history perspectives: the "system" and "causal" approaches. The authors assert that thecausal approach is more appropriate from an analytical point of view as it provides a straightforwardsolution to simultaneity, cause-effect lags, and temporal shapes of effects. Based on comparativecross-national applications in West and East Germany, Canada, Latvia and the Netherlands, wedemonstrate the usefulness of the causal approach by analyzing two highly interdependent famlyprocesses: entry into marriage (for individuals who are in a consensual union as the dependentprocess and first pregnancy/childbirth as the explaining one. Both statistical and theorteticalexplanations are explored emphasizing the need for conceptual reasoning.FrenchL’utilisation des processus interdépendants ou parallèles en tant que facteursexplicatifs dans des modèles des transitions aux quotients instantanés est une descontributions les plus importantes de l’analyse des biographies. Le but de cetarticle est d’appliquer une approche causale à l’analyse des événements familiauxinterdépendants. L’étude présente une typologie de processus parallèles et deuxperspectives de l’analyse des biographies: les approches ‘systémique’ et‘causale’. Les auteurs soutiennent que l’approche causale est plus appropriée dupoint de vue d’analyse. Elle offre une solution valable aux problèmes desimultanéité, les problèmes de décalage dans les intervalles entre la cause etl’effet, et, enfin, les problèmes des courbes temporelles modelées par les effets.L’utilité de cette
Missing data estimation in fMRI dynamic causal modeling.
Zaghlool, Shaza B; Wyatt, Christopher L
2014-01-01
Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.
Physiologically informed dynamic causal modeling of fMRI data.
Havlicek, Martin; Roebroeck, Alard; Friston, Karl; Gardumi, Anna; Ivanov, Dimo; Uludag, Kamil
2015-11-15
The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses - such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal
Directory of Open Access Journals (Sweden)
Hazuki Ishida
2013-01-01
Full Text Available This paper explores whether Japanese economy can continue to grow without extensive dependence on fossil fuels. The paper conducts time series analysis using a multivariate model of fossil fuels, non-fossil energy, labor, stock and GDP to investigate the relationship between fossil fuel consumption and economic growth in Japan. The results of cointegration tests indicate long-run relationships among the variables. Using a vector error-correction model, the study reveals bidirectional causality between fossil fuels and GDP. The results also show that there is no causal relationship between non-fossil energy and GDP. The results of cointegration analysis, Granger causality tests, and variance decomposition analysis imply that non-fossil energy may not necessarily be able to play the role of fossil fuels. Japan cannot seem to realize both continuous economic growth and the departure from dependence on fossil fuels. Hence, growth-oriented macroeconomic policies should be re-examined.
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…
Buchsbaum, Daphna; Seiver, Elizabeth; Bridgers, Sophie; Gopnik, Alison
2012-01-01
A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between
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.
Institute of Scientific and Technical Information of China (English)
吕思琪
2013-01-01
This paper intends to analyze the six types of English imperative sentences proposed by Chen (1984) from a perspective of causal-chain windowing. It comes to the conclusions that Talmy’s causal-chain windowing approach as well as the cognitive underpinnings of causal windowing and gapping is proved to be applicable in English imperative structures, and that generally speaking, the final portion of an imperative sentence is always windowed while the intermediate portions gapped.
Causal Effect Estimation Methods
2014-01-01
Relationship between two popular modeling frameworks of causal inference from observational data, namely, causal graphical model and potential outcome causal model is discussed. How some popular causal effect estimators found in applications of the potential outcome causal model, such as inverse probability of treatment weighted estimator and doubly robust estimator can be obtained by using the causal graphical model is shown. We confine to the simple case of binary outcome and treatment vari...
Possible detection of causality violation in a non-local scalar model
Energy Technology Data Exchange (ETDEWEB)
Haque, Asrarul; Joglekar, Satish D [Department of Physics, IIT Kanpur, Kanpur 208016 (India)], E-mail: ahaque@iitk.ac.in, E-mail: sdj@iitk.ac.in
2009-02-13
We consider the possibility that there may be causality violation detectable at higher energies. We take a scalar non-local theory containing a mass scale {lambda} as a model example and make a preliminary study of how the causality violation can be observed. We show how to formulate an observable whose detection would signal causality violation. We study the range of energies (relative to {lambda}) and couplings to which the observable can be used.
Granger causality for circular variables
Energy Technology Data Exchange (ETDEWEB)
Angelini, Leonardo; Pellicoro, Mario [Istituto Nazionale di Fisica Nucleare, Sezione di Bari (Italy); Dipartimento di Fisica, University of Bari (Italy); Stramaglia, Sebastiano, E-mail: sebastiano.stramaglia@ba.infn.i [Istituto Nazionale di Fisica Nucleare, Sezione di Bari (Italy); Dipartimento di Fisica, University of Bari (Italy)
2009-06-29
In this Letter we discuss the use of Granger causality to the analyze systems of coupled circular variables, by modifying a recently proposed method for multivariate analysis of causality. We show the application of the proposed approach on several Kuramoto systems, in particular one living on networks built by preferential attachment and a model for the transition from deeply to lightly anaesthetized states. Granger causalities describe the flow of information among variables.
Lannoy, N; Abinet, I; Bosmans, A; Lambert, C; Vermylen, C; Hermans, C
2012-05-01
Haemophilia A (HA) is caused by widespread mutations in the factor VIII gene. The high spontaneous mutation rate of this gene means that roughly 40% of HA mutations are private. This study aimed to describe the approaches used to confirm private disease-causing mutations in a cohort of Belgian HA patients. We studied 148 unrelated HA families for the presence of intron 22 and intron 1 inversion by Southern blotting and polymerase chain reaction (PCR). Multiplex ligation-dependent probe amplification (MLPA) assay was used to detect large genomic rearrangements. Detection of point mutations was performed by DNA sequencing. Predicting the causal impact of new non-synonymous changes was studied by two general strategies: (i) molecular approaches such as family cosegregation, evaluation of the implicated codon based on phylogenic separated species and absence of the mutation in the general Belgian population, and (ii) bioinformatics approaches to analyse the potential functional consequences of missense mutations. Among the 148 HA patients, in addition to common intron 22 and intron 1 inversions as well as large deletions or duplications, 67 different point mutations were identified, of which 42 had been reported in the HAMSTeRS database, and 25 were novel including 10 null variants for which RNA analyses confirmed the causal effect of mutations located in a splice site consensus and 15 missense mutations whose causality was demonstrated by molecular approaches and bioinformatics. This article reports several strategies to evaluate the deleterious consequences of unreported F8 substitutions in a large cohort of HA patients.
Siggiridou, Elsa
2015-01-01
Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different t...
Poppe, Michaela; Zitek, Andreas; Salles, Paulo; Bredeweg, Bert; Muhar, Susanne
2010-05-01
The education system needs strategies to attract future scientists and practitioners. There is an alarming decline in the number of students choosing science subjects. Reasons for this include the perceived complexity and the lack of effective cognitive tools that enable learners to acquire the expertise in a way that fits its qualitative nature. The DynaLearn project utilises a "Learning by modelling" approach to deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge. The modelling approach is based on qualitative reasoning, a research area within artificial intelligence, and allows for capturing and simulating qualitative systems knowledge. Educational activities within the DynaLearn software address topics at different levels of complexity, depending on the educational goals and settings. DynaLearn uses virtual characters in the learning environment as agents for engaging and motivating the students during their modelling exercise. The DynaLearn software represents an interactive learning environment in which learners are in control of their learning activities. The software is able to coach them individually based on their current progress, their knowledge needs and learning goals. Within the project 70 expert models on different environmental issues covering seven core topics (Earth Systems and Resources, The Living World, Human population, Land and Water Use, Energy Resources and Consumption, Pollution, and Global Changes) will be delivered. In the context of the core topic "Land and Water Use" the Institute of Hydrobiology and Aquatic Ecosystem Management has developed a model on Sustainable River Catchment Management. River systems with their catchments have been tremendously altered due to human pressures with serious consequences for the ecological integrity of riverine landscapes. The operation of hydropower plants, the implementation of flood protection measures, the regulation of flow and sediment regime and intensive
The causal pie model: an epidemiological method applied to evolutionary biology and ecology.
Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette
2014-05-01
A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made.
Moura, Lidia Mvr; Westover, M Brandon; Kwasnik, David; Cole, Andrew J; Hsu, John
2017-01-01
The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.
Causal Agency Theory: Reconceptualizing a Functional Model of Self-Determination
Shogren, Karrie A.; Wehmeyer, Michael L.; Palmer, Susan B.; Forber-Pratt, Anjali J.; Little, Todd J.; Lopez, Shane
2015-01-01
This paper introduces Causal Agency Theory, an extension of the functional model of self-determination. Causal Agency Theory addresses the need for interventions and assessments pertaining to selfdetermination for all students and incorporates the significant advances in understanding of disability and in the field of positive psychology since the…
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...
Dynamic causal models of neural system dynamics: current state and future extensions
Indian Academy of Sciences (India)
Klaas E Stephan; Lee M Harrison; Stefan J Kiebel; Olivier David; Will D Penny; Karl J Friston
2007-01-01
Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Cognitive Structure of Climate Information System Actors:Using Causal Mapping Approach
Directory of Open Access Journals (Sweden)
Maryam Sharifzadeh
2012-01-01
Full Text Available Promoting sustainability, productivity, efficiency, and development of agricultural sector are the functions of utilization of appropriate information in terms of agricultural climate information system (ACIS. In this regard, the main question is that, to what extent does the ACIS lead to or provide the necessary context for agricultural development? This research aimed to employ causal mapping approach to investigate cognitive structure of human actors in a climate information system. This explorative qualitative research used case study methodology. This paper is an examination and reflection upon analysis of qualitative data reports, with particular attention to the process of interactively elicited causal maps based on focus group interviews. An exploratory coding approach was used to identify concepts that emerged from the interview transcripts. The relevant knowledge is gathered through the tacit understandings of climate information producers (2 groups, extensionists (6 groups, and users (7 groups in Fars province to reach to the point of redundancy. Investigating causal maps revealed that, actors perceived climate information system challenges as economic, information processing, socio-political, organizational, and technical challenges. The study provided some suggestions to reach to a responsive short term and sustainable long term climate information system in Fars province.
Directory of Open Access Journals (Sweden)
Moura LMVR
2016-12-01
Full Text Available Lidia MVR Moura,1,2 M Brandon Westover,1,2 David Kwasnik,1 Andrew J Cole,1,2 John Hsu3–5 1Massachusetts General Hospital, Department of Neurology, Epilepsy Service, Boston, MA, USA; 2Harvard Medical School, Boston, MA, USA; 3Massachusetts General Hospital, Mongan Institute, Boston, MA, USA; 4Harvard Medical School, Department of Medicine, Boston, MA, USA; 5Harvard Medical School, Department of Health Care Policy, Boston, MA, USA Abstract: The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer’s disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions. Keywords: epilepsy, epidemiology, neurostatistics, causal inference
Hayduk, Leslie
2014-01-01
Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously…
Causal Indicator Models Have Nothing to Do with Measurement
Howell, Roy D.; Breivik, Einar
2016-01-01
In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…
Causal inference in complex longitudinal models: the continuous case
Robins, J.M.
2001-01-01
We extend Robins' theory of causal inference for complex longitudinal data to the case of continuously varying as opposed to discrete covariates and treatments. In particular we establish versions of the key results of the discrete theory: the g-computation formula and a collection of powerful chara
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
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.
Causal Dynamical Triangulations in the Spincube Model of Quantum Gravity
Vojinovic, Marko
2015-01-01
We study the implications of the simplicity constraint in the spincube model of quantum gravity. Relating the edge-lengths to integer triangle areas, the simplicity constraint imposes a very strong restrictions between them, ultimately leading to a requirement that all 4-simplices in the triangulation must be almost mutually identical. As a surprising and unexpected consequence of this property, one can obtain the CDT state sum as a special case of the spincube state sum. This relationship brings new insight into the long-standing problem of the relationship between the spinfoam approach and the CDT approach to quantum gravity. In particular, it turns out that the spincube model contains properties of both approaches, providing a single unifying framework for their analysis and comparison. In addition, the spincube state sum also contains some other special cases, very similar but not equivalent to the CDT state sum.
A Pitfall in Using the Characterization of Granger Non-Causality in Vector Autoregressive Models
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Umberto Triacca
2015-04-01
Full Text Available It is well known that in a vector autoregressive (VAR model Granger non-causality is characterized by a set of restrictions on the VAR coefficients. This characterization has been derived under the assumption of non-singularity of the covariance matrix of the innovations. This note shows that if this assumption is violated, then the characterization of Granger non-causality in a VAR model fails to hold. In these situations Granger non-causality test results must be interpreted with caution.
Causality between stock price and GDP in Turkey: An ARDL Bounds Testing Approach
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Turgut Tursoy
2016-12-01
Full Text Available The study investigates the dynamic relationship between stock prices and GDP in Turkey using quarterly data from 1989Q2-2014Q2. The study investigated the interrelationship between the variables via auto regressive distributive lag (ARDL framework and ECM to analyse the existence of a long-run equilibrium relationship between gross domestic product and stock prices. The results provide strong evidence that both the stock prices and GDP are strongly cointegrated in the long-run. The empirical estimation indicated a significantly positive relationship between GDP and stock prices. The robustness of the ARDL model was confirmed by using Johansen and Juselius’s cointegration test (1990. The Granger causality test results indicate a long-run bidirectional causality between stock prices and GDP, and also a uni-directional causality from GDP to stock prices in the short-run. Both the stock prices and the economic growth are directly linked with each other. The reliability and validity of our estimations are confirmed by the diagnostics and the CUSUM test.
Energy Technology Data Exchange (ETDEWEB)
Wolde-Rufael, Yemane
2010-01-15
This paper attempts to examine the dynamic relationship between economic growth, nuclear energy consumption, labor and capital for India for the period 1969-2006. Applying the bounds test approach to cointegration developed by we find that there was a short- and a long-run relationship between nuclear energy consumption and economic growth. Using four long-run estimators we also found that nuclear energy consumption has a positive and a statistically significant impact on India's economic growth. Further, applying the approach to Granger causality and the variance decomposition approach developed by, we found a positive and a significant uni-directional causality running from nuclear energy consumption to economic growth without feedback. This implies that economic growth in India is dependent on nuclear energy consumption where a decrease in nuclear energy consumption may lead to a decrease in real income. For a fast growing energy-dependent economy this may have far-reaching implications for economic growth. India's economic growth can be frustrated if energy conservation measures are undertaken without due regard to the negative impact they have on economic growth. (author)
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.
A restricted dimer model on a 2-dimensional random causal triangulation
Ambjorn, J; Wheater, J F
2014-01-01
We introduce a restricted hard dimer model on a random causal triangulation that is exactly solvable and generalizes a model recently proposed by Atkin and Zohren. We show that the latter model exhibits unusual behaviour at its multicritical point; in particular, its Hausdorff dimension equals 3 and not 3/2 as would be expected from general scaling arguments. When viewed as a special case of the generalized model introduced here we show that this behaviour is not generic and therefore is not likely to represent the true behaviour of the full dimer model on a random causal triangulation.
Directory of Open Access Journals (Sweden)
Marinela eCapanu
2015-05-01
Full Text Available Identifying the small number of rare causal variants contributing to disease has beena major focus of investigation in recent years, but represents a formidable statisticalchallenge due to the rare frequencies with which these variants are observed. In thiscommentary we draw attention to a formal statistical framework, namely hierarchicalmodeling, to combine functional genomic annotations with sequencing data with theobjective of enhancing our ability to identify rare causal variants. Using simulations weshow that in all configurations studied, the hierarchical modeling approach has superiordiscriminatory ability compared to a recently proposed aggregate measure of deleteriousness,the Combined Annotation-Dependent Depletion (CADD score, supportingour premise that aggregate functional genomic measures can more accurately identifycausal variants when used in conjunction with sequencing data through a hierarchicalmodeling approach
Campbell's and Rubin's Perspectives on Causal Inference
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
Inferring tree causal models of cancer progression with probability raising.
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Loes Olde Loohuis
Full Text Available Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models.
Chapman, A. . Camels, diamonds and counterfactuals : a model for teaching causal reasoning
Weijs, Marijke
2011-01-01
In het artikel ‘Camels, diamonds and counterfactuals: a model for teaching causal reasoning’ beschrijft Chapman een onderwijsmodel voor vooruitgang in oorzakelijk redeneren. Dit model is bedoeld voor 16+-leerlingen die met dit model worden toegerust om een robuuste oorzakelijke analyse te maken. Cha
A restricted dimer model on a two-dimensional random causal triangulation
DEFF Research Database (Denmark)
Ambjørn, Jan; Durhuus, Bergfinnur; Wheater, J. F.
2014-01-01
We introduce a restricted hard dimer model on a random causal triangulation that is exactly solvable and generalizes a model recently proposed by Atkin and Zohren (2012 Phys. Lett. B 712 445–50). We show that the latter model exhibits unusual behaviour at its multicritical point; in particular, its...
Rabbitt, Matthew P.
2016-11-01
Social scientists are often interested in examining causal relationships where the outcome of interest is represented by an intangible concept, such as an individual's well-being or ability. Estimating causal relationships in this scenario is particularly challenging because the social scientist must rely on measurement models to measure individual's properties or attributes and then address issues related to survey data, such as omitted variables. In this paper, the usefulness of the recently proposed behavioural Rasch selection model is explored using a series of Monte Carlo experiments. The behavioural Rasch selection model is particularly useful for these types of applications because it is capable of estimating the causal effect of a binary treatment effect on an outcome that is represented by an intangible concept using cross-sectional data. Other methodology typically relies of summary measures from measurement models that require additional assumptions, some of which make these approaches less efficient. Recommendations for application of the behavioural Rasch selection model are made based on results from the Monte Carlo experiments.
Besson, Ugo
2010-01-01
This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought…
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.
Critical Thinking and Political Participation: The Development and Assessment of a Causal Model.
Guyton, Edith M.
An assessment of a four-stage conceptual model reveals that critical thinking has indirect positive effects on political participation through its direct effects on personal control, political efficacy, and democratic attitudes. The model establishes causal relationships among selected personality variables (self-esteem, personal control, and…
The control outcome calibration approach for causal inference with unobserved confounding.
Tchetgen Tchetgen, Eric
2014-03-01
Unobserved confounding can seldom be ruled out with certainty in nonexperimental studies. Negative controls are sometimes used in epidemiologic practice to detect the presence of unobserved confounding. An outcome is said to be a valid negative control variable to the extent that it is influenced by unobserved confounders of the exposure effects on the outcome in view, although not directly influenced by the exposure. Thus, a negative control outcome found to be empirically associated with the exposure after adjustment for observed confounders indicates that unobserved confounding may be present. In this paper, we go beyond the use of control outcomes to detect possible unobserved confounding and propose to use control outcomes in a simple but formal counterfactual-based approach to correct causal effect estimates for bias due to unobserved confounding. The proposed control outcome calibration approach is developed in the context of a continuous or binary outcome, and the control outcome and the exposure can be discrete or continuous. A sensitivity analysis technique is also developed, which can be used to assess the degree to which a violation of the main identifying assumption of the control outcome calibration approach might impact inference about the effect of the exposure on the outcome in view.
Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform.
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Ahmed E Enayetallah
Full Text Available Triglyceride accumulation is associated with obesity and type 2 diabetes. Genetic disruption of diacylglycerol acyltransferase 1 (DGAT1, which catalyzes the final reaction of triglyceride synthesis, confers dramatic resistance to high-fat diet induced obesity. Hence, DGAT1 is considered a potential therapeutic target for treating obesity and related metabolic disorders. However, the molecular events shaping the mechanism of action of DGAT1 pharmacological inhibition have not been fully explored yet. Here, we investigate the metabolic molecular mechanisms induced in response to pharmacological inhibition of DGAT1 using a recently developed computational systems biology approach, the Causal Reasoning Engine (CRE. The CRE algorithm utilizes microarray transcriptomic data and causal statements derived from the biomedical literature to infer upstream molecular events driving these transcriptional changes. The inferred upstream events (also called hypotheses are aggregated into biological models using a set of analytical tools that allow for evaluation and integration of the hypotheses in context of their supporting evidence. In comparison to gene ontology enrichment analysis which pointed to high-level changes in metabolic processes, the CRE results provide detailed molecular hypotheses to explain the measured transcriptional changes. CRE analysis of gene expression changes in high fat habituated rats treated with a potent and selective DGAT1 inhibitor demonstrate that the majority of transcriptomic changes support a metabolic network indicative of reversal of high fat diet effects that includes a number of molecular hypotheses such as PPARG, HNF4A and SREBPs. Finally, the CRE-generated molecular hypotheses from DGAT1 inhibitor treated rats were found to capture the major molecular characteristics of DGAT1 deficient mice, supporting a phenotype of decreased lipid and increased insulin sensitivity.
Dijk, van, Nico M.; Breedveld, P.C.
1991-01-01
The existence of zero-order causal paths in bond graphs of physical systems implies the set of state equations to be an implicit mixed set of Differential and Algebraic Equations (DAEs). In the block diagram expansion of such a bond graph, this type of causal path corresponds with a zero-order loop. In this paper the numerical solution of the DAEs by methods commonly used for solving stiff systems of Ordinary Differential Equations (ODEs) is discussed. Apart from a description of the numerica...
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.
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Irina A. Mironenko
2009-01-01
Full Text Available Russian psychology has brought into the world science at least two great ideas: the conditioned reflex (Pavlov and the zone of proximal development (Vygotsky. These concepts were formulated before “iron curtain” fell. Since then Russian science dropped out from the view of western colleagues for decades. Now it is challenged to re-join international mainstream. Are we in a position to contribute?A key concept for Russian psychology is personality impact on psycho-physiological functions and causal approach to self-determination. The concept of selfdetermination appeared in Western theories in 1980-es and since then it has been developed in the context of teleological humanitarian approach. In Russian science the concept of self-determination dates back to 1934, when it was defined by Rubinstein as “sub’ekt”. Self-determination of ontogenesis of psycho physiological functions resulting from confluence of ontogenesis and social development was explicated by Russian scientists whose theoretical reasoning and empirical results are compared to Western counterparts.
The impact of school leadership on school level factors: validation of a causal model
M.L. Krüger; B. Witziers; P. Sleegers
2007-01-01
This study aims to contribute to a better understanding of the antecedents and effects of educational leadership, and of the influence of the principal's leadership on intervening and outcome variables. A path analysis was conducted to test and validate a causal model. The results show no direct or
Calsyn, Robert J.; Winter, Joel P.; Burger, Gary K.
2005-01-01
This study compared the strength of competing causal models in explaining the relationship between perceived support, enacted support, and social anxiety in adolescents. The social causation hypothesis postulates that social support causes social anxiety, whereas the social selection hypothesis postulates that social anxiety causes social support.…
Predicting Adaptive Performance in Multicultural Teams: A Causal Model
2008-02-01
Applied Psychology, 91, 1189-1207. [6] Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah...means of Factor Analysis (FA), Multidimensional Scaling (MDS), and Structural Equation Modeling (LISREL). Unpublished manuscript; in process of being... equation modeling . New York, NY: Guilford Press. [14] Kozlowski, S. W. J., Gully, S. M., Brown, K. G., Salas, E., Smith, E. M., & Nason, E. R. (2001
The Epstein–Glaser causal approach to the light-front QED{sub 4}. II: Vacuum polarization tensor
Energy Technology Data Exchange (ETDEWEB)
Bufalo, R., E-mail: rodrigo.bufalo@helsinki.fi [Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki (Finland); Instituto de Física Teórica (IFT/UNESP), UNESP - São Paulo State University, Rua Dr. Bento Teobaldo Ferraz 271, Bloco II Barra Funda, CEP 01140-070 São Paulo, SP (Brazil); Pimentel, B.M., E-mail: pimentel@ift.unesp.br [Instituto de Física Teórica (IFT/UNESP), UNESP - São Paulo State University, Rua Dr. Bento Teobaldo Ferraz 271, Bloco II Barra Funda, CEP 01140-070 São Paulo, SP (Brazil); Soto, D.E., E-mail: danielsb@ift.unesp.br [Instituto de Física Teórica (IFT/UNESP), UNESP - São Paulo State University, Rua Dr. Bento Teobaldo Ferraz 271, Bloco II Barra Funda, CEP 01140-070 São Paulo, SP (Brazil)
2014-12-15
In this work we show how to construct the one-loop vacuum polarization for light-front QED{sub 4} in the framework of the perturbative causal theory. Usually, in the canonical approach, it is considered for the fermionic propagator the so-called instantaneous term, but it is known in the literature that this term is controversial because it can be omitted by computational reasons; for instance, by compensation or vanishing by dimensional regularization. In this work we propose a solution to this paradox. First, in the Epstein–Glaser causal theory, it is shown that the fermionic propagator does not have instantaneous term, and with this propagator we calculate the one-loop vacuum polarization, from this calculation it follows the same result as those obtained by the standard approach, but without reclaiming any extra assumptions. Moreover, since the perturbative causal theory is defined in the distributional framework, we can also show the reason behind our obtaining the same result whether we consider or not the instantaneous fermionic propagator term. - Highlights: • We develop the Epstein–Glaser causal approach for light-front field theory. • We evaluate in detail the vacuum polarization at one-loop for the light-front QED. • We discuss the subtle issues of the Instantaneous part of the fermionic propagator in the light-front. • We evaluate the vacuum polarization at one-loop for the light-front QED with the Instantaneous fermionic part.
Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery
Zhang, Kun; Janzing, Dominik
2012-01-01
In nonlinear latent variable models or dynamic models, if we consider the latent variables as confounders (common causes), the noise dependencies imply further relations between the observed variables. Such models are then closely related to causal discovery in the presence of nonlinear confounders, which is a challenging problem. However, generally in such models the observation noise is assumed to be independent across data dimensions, and consequently the noise dependencies are ignored. In this paper we focus on the Gaussian process latent variable model (GPLVM), from which we develop an extended model called invariant GPLVM (IGPLVM), which can adapt to arbitrary noise covariances. With the Gaussian process prior put on a particular transformation of the latent nonlinear functions, instead of the original ones, the algorithm for IGPLVM involves almost the same computational loads as that for the original GPLVM. Besides its potential application in causal discovery, IGPLVM has the advantage that its estimat...
mediation: R Package for Causal Mediation Analysis
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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.
Verification of temporal-causal network models by mathematical analysis
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Jan Treur
2016-04-01
Full Text Available Abstract Usually dynamic properties of models can be analysed by conducting simulation experiments. But sometimes, as a kind of prediction properties can also be found by calculations in a mathematical manner, without performing simulations. Examples of properties that can be explored in such a manner are: whether some values for the variables exist for which no change occurs (stationary points or equilibria, and how such values may depend on the values of the parameters of the model and/or the initial values for the variables whether certain variables in the model converge to some limit value (equilibria and how this may depend on the values of the parameters of the model and/or the initial values for the variables whether or not certain variables will show monotonically increasing or decreasing values over time (monotonicity how fast a convergence to a limit value takes place (convergence speed whether situations occur in which no convergence takes place but in the end a specific sequence of values is repeated all the time (limit cycle Such properties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments. If one of these properties is not fulfilled, then there will be some error in the implementation of the model. In this paper some methods to analyse such properties of dynamical models will be described and illustrated for the Hebbian learning model, and for dynamic connection strengths in social networks. The properties analysed by the methods discussed cover equilibria, increasing or decreasing trends, recurring patterns (limit cycles, and speed of convergence to equilibria.
Relationship of causal effects in a causal chain and related inference
Institute of Scientific and Technical Information of China (English)
GENG Zhi; HE Yangbo; WANG Xueli
2004-01-01
This paper discusses the relationship among the total causal effect and local causal effects in a causal chain and identifiability of causal effects. We show a transmission relationship of causal effects in a causal chain. According to the relationship, we give an approach to eliminating confounding bias through controlling for intermediate variables in a causal chain.
Causality issues of particle detector models in QFT and Quantum Optics
Martin-Martinez, Eduardo
2015-01-01
We analyze the constraints that causality imposes on some of the particle detector models employed in quantum field theory in general, and in particular on those used in quantum optics (or superconducting circuits) to model atoms interacting with light. Namely, we show that disallowing faster-than-light communication can impose severe constraints on the applicability of particle detector models in three different common scenarios: 1) when the detectors are spatially smeared, 2) when a UV cutoff is introduced in the theory and 3) under one of the most typical approximations made in quantum optics: the rotating-wave approximation. We identify in which scenarios the models' causal behaviour can be cured and in which it cannot.
Concurrency Models with Causality and Events as Psi-calculi
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Håkon Normann
2014-10-01
Full Text Available Psi-calculi are a parametric framework for nominal calculi, where standard calculi are found as instances, like the pi-calculus, or the cryptographic spi-calculus and applied-pi. Psi-calculi have an interleaving operational semantics, with a strong foundation on the theory of nominal sets and process algebras. Much of the expressive power of psi-calculi comes from their logical part, i.e., assertions, conditions, and entailment, which are left quite open thus accommodating a wide range of logics. We are interested in how this expressiveness can deal with event-based models of concurrency. We thus take the popular prime event structures model and give an encoding into an instance of psi-calculi. We also take the recent and expressive model of Dynamic Condition Response Graphs (in which event structures are strictly included and give an encoding into another corresponding instance of psi-calculi. The encodings that we achieve look rather natural and intuitive. Additional results about these encodings give us more confidence in their correctness.
Exact solutions of a Flat Full Causal Bulk viscous FRW cosmological model through factorization
Cornejo-Pérez, O
2012-01-01
We study the classical flat full causal bulk viscous FRW cosmological model through the factorization method. The method allows to find some new exact parametric solutions for different values of the viscous parameter $s$. Special attention is given to the well known case $s=1/2$, for which the cosmological model admits scaling symmetries. Also, some exact parametric solutions for $s=1/2$ are obtained through the Lie group method.
Learning World Models in Environments with Manifest Causal Structure,
1995-05-01
an agent with no prior knowledge than for people because people are told much of what they need to know and do not learn tabula rasa . Many people nd...drafts of this thesis, and for being a great role model. Thanks to Eric Grimson for being much more than an academic advisor. I thank Jonathan Amsterdam...early training of the secretary robot, the trainer plays the role of a babysitter more than that of a teacher. The trainer is available in case of an
Directory of Open Access Journals (Sweden)
Grauls D.
2006-12-01
Full Text Available Abnormal fluid pressure regimes are commonly encountered at depth in most sedimentary basins. Relationships between effective vertical stress and porosity have been applied, since 1970 to the Gulf Coast area, to assess the magnitude of overpressures. Positive results have been obtained from seismic and basin-modeling techniques in sand-shale, vertical-stress-dominated tertiary basins, whenever compaction disequilibrium conditions apply. However, overpressures resulting from other and/or additional causes (tectonic stress, hydrocarbon generation, thermal stress, fault-related transfer, hydrofracturing. . . cannot be quantitatively assessed using this approach. A hydromechanical approach is then proposed in addition to conventional methods. At any depth, the upper bound fluid pressure is controlled by in situ conditions related to hydrofracturing or fault reactivation. Fluid-driven fracturing implies an episodically open system, under a close to zerominimum effective stress regime. Sound knowledge of present-day tectonic stress regimes allows a direct estimation of minimum stress evolution. A quantitative fluid pressure assessment at depth is therefore possible, as in undrained or/and compartmented geological systems, pressure regimes, whatever their origin, tend to rapidly reach a value close to the minimum principal stress. Therefore, overpressure assessment will be improved, as this methodology can be applied to various geological settings and situations where present-day overpressures originated from other causal mechanisms, very often combined. However, pressure trends in transition zones are more difficult to assess correctly. Additional research on cap rocks and fault seals is therefore required to improve their predictability. In addition to overpressure assessment, the minimum principal stress concept allows a better understanding of petroleum system, as fault-related hydrocarbon dynamic transfers, hydrofractured domains and cap
Performing Causal Configurations in e-Tourism: a Fuzzy-Set Approach
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Hugues Seraphin
2016-07-01
Full Text Available Search engines are constantly endeavouring to integrate social media mentions in the website ranking process. Search Engine Optimization (SEO principles can be used to impact website ranking, considering various social media channels� capability to drive traffic. Both practitioners and researchers has focused on the impact of social media on SEO, but paid little attention to the influences of social media interactions on organic search results. This study explores the causal configurations between social mention variables (strength, sentiment, passion, reach and the rankings of nine websites dedicated to hotel booking (according to organic search results. The social mention variables embedded into the conceptual model were provided by the real-time social media search and analysis tool (www.socialmention.com, while the rankings websites dedicated to hotel booking were determined after a targeted search on Google. The study employs fuzzy-set qualitative comparative analysis (fsQCA and the results reveal that social mention variables has complex links with the rankings of the hotel booking websites included into the sample, according to Quine-McCluskey algorithm solution. The findings extend the body of knowledge related to the impact of social media mentions on
Chee-Yin, Yip; Hock-Eam, Lim
2014-12-01
This paper examines using housing supply as proxy to house prices, the causal relationship on house prices among 8 states in Malaysia by applying the Engle-Granger cointegration test and Granger causality test approach. The target states are Perak, Selangor, Penang, Federal Territory of Kuala Lumpur (WPKL or Kuala Lumpur), Kedah, Negeri Sembilan, Sabah and Sarawak. The primary aim of this study is to estimate how long (in months) house prices in Perak lag behind that of Selangor, Penang and WPKL. We classify the 8 states into two categories - developed and developing states. We use Engle-Granger cointegration test and Granger causality test to examine the long run and short run equilibrium relationship among the two categories.. It is found that the causal relationship is bidirectional in Perak and Sabah, Perak and Selangor while it is unidirectional for Perak and Sarawak, Perak and Penang, Perak and WPKL. The speed of deviation adjustment is about 273%, suggesting that the pricing dynamic of Perak has a 32- month or 2 3/4- year lag behind that of WPKL, Selangor and Penang. Such information will be useful to investors, house buyers and speculators.
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Rohin Anhal
2013-10-01
Full Text Available The aim of this paper is to examine the direction of causality between real GDP on the one hand and final energy and coal consumption on the other in India, for the period from 1970 to 2011. The methodology adopted is the non-parametric bootstrap procedure, which is used to construct the critical values for the hypothesis of causality. The results of the bootstrap tests show that for total energy consumption, there exists no causal relationship in either direction with GDP of India. However, if coal consumption is considered, we find evidence in support of unidirectional causality running from coal consumption to GDP. This clearly has important implications for the Indian economy. The most important implication is that curbing coal consumption in order to reduce carbon emissions would in turn have a limiting effect on economic growth. Our analysis contributes to the literature in three distinct ways. First, this is the first paper to use the bootstrap method to examine the growth-energy connection for the Indian economy. Second, we analyze data for the time period 1970 to 2011, thereby utilizing recently available data that has not been used by others. Finally, in contrast to the recently done studies, we adopt a disaggregated approach for the analysis of the growth-energy nexus by considering not only aggregate energy consumption, but coal consumption as well.
Calibrating the pixel-level Kepler imaging data with a causal data-driven model
Wang, Dun; Hogg, David W; Schölkopf, Bernhard
2015-01-01
Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science---the most precise photometric measurements of stars ever made---appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits in the target star. In addition, we use the target star's future and past (auto-regr...
A new approach in classical electrodynamics to protect principle of causality
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Biswaranjan Dikshit
2014-03-01
Full Text Available In classical electrodynamics, electromagnetic effects are calculated from solution of wave equation formed by combination of four Maxwell’s equations. However, along with retarded solution, this wave equation admits advanced solution in which case the effect happens before the cause. So, to preserve causality in natural events, the retarded solution is intentionally chosen and the advance part is just ignored. But, an equation or method cannot be called fundamental if it admits a wrong result (that violates principle of causality in addition to the correct result. Since it is the Maxwell’s form of equations that gives birth to this acausal advanced potential, we rewrite these equations in a different form using the recent theory of reaction at a distance (Biswaranjan Dikshit, Physics essays, 24(1, 4-9, 2011 so that the process of calculation does not generate any advanced effects. Thus, the long-standing causality problem in electrodynamics is solved.
Recursive causality in evolution: a model for epigenetic mechanisms in cancer development.
Haslberger, A; Varga, F; Karlic, H
2006-01-01
Interactions between adaptative and selective processes are illustrated in the model of recursive causality as defined in Rupert Riedl's systems theory of evolution. One of the main features of this theory also termed as theory of evolving complexity is the centrality of the notion of 'recursive' or 'feedback' causality - 'the idea that every biological effect in living systems, in some way, feeds back to its own cause'. Our hypothesis is that "recursive" or "feedback" causality provides a model for explaining the consequences of interacting genetic and epigenetic mechanisms which are known to play a key role in development of cancer. Epigenetics includes any process that alters gene activity without changes of the DNA sequence. The most important epigenetic mechanisms are DNA-methylation and chromatin remodeling. Hypomethylation of so-called oncogenes and hypermethylation of tumor suppressor genes appear to be critical determinants of cancer. Folic acid, vitamin B12 and other nutrients influence the function of enzymes that participate in various methylation processes by affecting the supply of methyl groups into a variety of molecules which may be directly or indirectly associated with cancerogenesis. We present an example from our own studies by showing that vitamin D3 has the potential to de-methylate the osteocalcin-promoter in MG63 osteosarcoma cells. Consequently, a stimulation of osteocalcin synthesis can be observed. The above mentioned enzymes also play a role in development and differentiation of cells and organisms and thus illustrate the close association between evolutionary and developmental mechanisms. This enabled new ways to understand the interaction between the genome and environment and may improve biomedical concepts including environmental health aspects where epigenetic and genetic modifications are closely associated. Recent observations showed that methylated nucleotides in the gene promoter may serve as a target for solar UV
Energy Technology Data Exchange (ETDEWEB)
Ciarreta, A. [Department of Economic Analysis II, University of the Basque Country (UPV/EHU), Avda, Lehendakari Aguirre, 83, 48015 Bilbao (Spain); Zarraga, A. [Department of Applied Economics III, University of the Basque Country (UPV/EHU), Avda, Lehendakari Aguirre, 83, 48015 Bilbao (Spain)
2010-07-15
This paper applies recent panel methodology to investigate the long-run and causal relationship between electricity consumption and real GDP for a set of 12 European countries using annual data for the period 1970-2007. The sample countries have moved faster than other neighboring countries towards the creation of a single electricity market over the past 30 years. Energy prices are also included in the study due to their important role in affecting the above variables, thus avoiding the problem of omitted variable bias. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a trivariate VECM estimated by system GMM. The results show evidence of a long-run equilibrium relationship between the three series and a negative short-run and strong causality from electricity consumption to GDP. As expected, there is bidirectional causality between energy prices and GDP and weaker evidence between electricity consumption and energy prices. These results support the policies implemented towards the creation of a common European electricity market. (author)
Energy Technology Data Exchange (ETDEWEB)
Ciarreta, A., E-mail: aitor.ciarreta@ehu.e [Department of Economic Analysis II, University of the Basque Country (UPV/EHU), Avda, Lehendakari Aguirre, 83, 48015 Bilbao (Spain); Zarraga, A., E-mail: ainhoa.zarraga@ehu.e [Department of Applied Economics III, University of the Basque Country (UPV/EHU), Avda, Lehendakari Aguirre, 83, 48015 Bilbao (Spain)
2010-07-15
This paper applies recent panel methodology to investigate the long-run and causal relationship between electricity consumption and real GDP for a set of 12 European countries using annual data for the period 1970-2007. The sample countries have moved faster than other neighboring countries towards the creation of a single electricity market over the past 30 years. Energy prices are also included in the study due to their important role in affecting the above variables, thus avoiding the problem of omitted variable bias. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a trivariate VECM estimated by system GMM. The results show evidence of a long-run equilibrium relationship between the three series and a negative short-run and strong causality from electricity consumption to GDP. As expected, there is bidirectional causality between energy prices and GDP and weaker evidence between electricity consumption and energy prices. These results support the policies implemented towards the creation of a common European electricity market.
A Bayesian network approach for causal inferences in pesticide risk assessment and management
Pesticide risk assessment and management must balance societal benefits and ecosystem protection, based on quantified risks and the strength of the causal linkages between uses of the pesticide and socioeconomic and ecological endpoints of concern. A Bayesian network (BN) is a gr...
From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology
Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.
2015-01-01
In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.
From animal model to human brain networking: dynamic causal modeling of motivational systems.
Gonen, Tal; Admon, Roee; Podlipsky, Ilana; Hendler, Talma
2012-05-23
An organism's behavior is sensitive to different reinforcements in the environment. Based on extensive animal literature, the reinforcement sensitivity theory (RST) proposes three separate neurobehavioral systems to account for such context-sensitive behavior, affecting the tendency to react to punishment, reward, or goal-conflict stimuli. The translation of animal findings to complex human behavior, however, is far from obvious. To examine whether the neural networks underlying humans' motivational processes are similar to those proposed by the RST model, we conducted a functional MRI study, in which 24 healthy subjects performed an interactive game that engaged the different motivational systems using distinct time periods (states) of punishment, reward, and conflict. Crucially, we found that the different motivational states elicited activations in brain regions that corresponded exactly to the brain systems underlying RST. Moreover, dynamic causal modeling of each motivational system confirmed that the coupling strengths between the key brain regions of each system were enabled selectively by the appropriate motivational state. These results may shed light on the impairments that underlie psychopathologies associated with dysfunctional motivational processes and provide a translational validity for the RST.
Carbon Emissions and Economic Growth: Causality Testing in Heterogenous Panels
Energy Technology Data Exchange (ETDEWEB)
David Maddison; Katrin Rehdanz [Department of Economics, University of Birmingham, Birmingham (United Kingdom)
2008-09-30
Numerous papers have examined data on energy and GDP for evidence of Granger causality. Using time series techniques these analyses not infrequently reach differing conclusions concerning the existence and direction of Granger causality. This paper presents a heterogenous panel approach to Granger causality testing. This technique is used to examine a panel of data for evidence of a causal relationship between GDP and carbon emissions per capita allowing for heterogeneity in short run dynamics and even the long run cointegrating vector. This technique is compared to the standard fixed dynamic effects approach to pooling individual error correction models. In one important case the heterogenous panel test for Granger causality reaches conclusions quite different to those from conventional tests of Granger causality. Except for Asia there is strong evidence for the existence of a bidirectional causal relationship between GDP per capita and CO{sub 2} emissions per capita.
Exploratory Causal Analysis in Bivariate Time Series Data
McCracken, James M.
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data
An introduction to causal inference.
Pearl, Judea
2010-02-26
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.
Alanko, Katarina; Santtila, Pekka; Salo, Benny; Jern, Patrik; Johansson, Ada; Sandnabba, N Kenneth
2011-06-01
An association between childhood gender atypical behaviour (GAB) and a negative parent-child relationship has been demonstrated in several studies, yet the causal relationship of this association is not fully understood. In the present study, different models of causation between childhood GAB and parent-child relationships were tested. Direction of causation modelling was applied to twin data from a population-based sample (n= 2,565) of Finnish 33- to 43-year-old twins. Participants completed retrospective self-report questionnaires. Five different models of causation were then fitted to the data: GAB → parent-child relationship, parent-child relationship → GAB, reciprocal causation, a bivariate genetic model, and a model assuming no correlation. It was found that a model in which GAB and quality of mother-child, and father-child relationship reciprocally affect each other best fitted the data. The findings are discussed in light of how we should understand, including causality, the association between GAB and parent-child relationship.
Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa
2017-01-01
Achieving a long-term food security and preventing hunger include a better nutrition through sustainable systems of production, distribution, and consumption. Nonetheless, the quest for an alternative to increasing global food supply to meet the growing demand has led to the use of poor agricultural practices that promote climate change. Given the contribution of the agricultural ecosystem towards greenhouse gas (GHG) emissions, this study investigated the causal nexus between carbon dioxide emissions and agricultural ecosystem by employing a data spanning from 1961 to 2012. Evidence from long-run elasticity shows that a 1 % increase in the area of rice paddy harvested will increase carbon dioxide emissions by 1.49 %, a 1 % increase in biomass-burned crop residues will increase carbon dioxide emissions by 1.00 %, a 1 % increase in cereal production will increase carbon dioxide emissions by 1.38 %, and a 1 % increase in agricultural machinery will decrease carbon dioxide emissions by 0.09 % in the long run. There was a bidirectional causality between carbon dioxide emissions, cereal production, and biomass-burned crop residues. The Granger causality shows that the agricultural ecosystem in Ghana is sensitive to climate change vulnerability.
Missing Data as a Causal and Probabilistic Problem
2015-07-01
graphs and missingness models to formally define missing data as a type of causal inference problem where only interventions on certain variables are...approach of [7] of representing missing data problems to causal models where only interventions on missingness indicators are allowed. We further use this... Missing Data as a Causal and Probabilistic Problem Ilya Shpitser Mathematical Sciences University of Southampton Southampton, UK SO14 6WD i.shpitser
Komperda, Regis
The purpose of this dissertation is to test a model of relationships among factors characterizing aspects of a student-centered constructivist learning environment and student outcomes of satisfaction and academic achievement in introductory undergraduate chemistry courses. Constructivism was chosen as the theoretical foundation for this research because of its widespread use in chemical education research and practice. In a constructivist learning environment the role of the teacher shifts from delivering content towards facilitating active student engagement in activities that encourage individual knowledge construction through discussion and application of content. Constructivist approaches to teaching introductory chemistry courses have been adopted by some instructors as a way to improve student outcomes, but little research has been done on the causal relationships among particular aspects of the learning environment and student outcomes. This makes it difficult for classroom teachers to know which aspects of a constructivist teaching approach are critical to adopt and which may be modified to better suit a particular learning environment while still improving student outcomes. To investigate a model of these relationships, a survey designed to measure student perceptions of three factors characterizing a constructivist learning environment in online courses was adapted for use in face-to-face chemistry courses. These three factors, teaching presence, social presence, and cognitive presence, were measured using a slightly modified version of the Community of Inquiry (CoI) instrument. The student outcomes investigated in this research were satisfaction and academic achievement, as measured by standardized American Chemical Society (ACS) exam scores and course grades. Structural equation modeling (SEM) was used to statistically model relationships among the three presence factors and student outcome variables for 391 students enrolled in six sections of a
Institute of Scientific and Technical Information of China (English)
魏岳嵩
2015-01-01
文章利用图模型方法分析结构向量自回归模型变量间的因果性问题，构建结构向量自回归因果图，研究该因果图的性质，基于信息论方法建立了因果图结构辨识的三步准则，并用所给方法做了实例分析。%This paper explores how to use graphical modelling approach to analyze the causal relations among variables of structure vector autoregressive model. The causal graphs of structure vector autoregressive model is established and its properties are investigated. A three-step procedure based on information theory criteria is developed to identify the causal structure of the causal graphs.Finally,a case analysis is presented using the propose method.
Neural pathways in processing of sexual arousal: a dynamic causal modeling study.
Seok, J-W; Park, M-S; Sohn, J-H
2016-09-01
Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.
On Causality in Dynamical Systems
Harnack, Daniel
2016-01-01
Identification of causal links is fundamental for the analysis of complex systems. In dynamical systems, however, nonlinear interactions may hamper separability of subsystems which poses a challenge for attempts to determine the directions and strengths of their mutual influences. We found that asymmetric causal influences between parts of a dynamical system lead to characteristic distortions in the mappings between the attractor manifolds reconstructed from respective local observables. These distortions can be measured in a model-free, data-driven manner. This approach extends basic intuitions about cause-effect relations to deterministic dynamical systems and suggests a mathematically well defined explanation of results obtained from previous methods based on state space reconstruction.
Cortês, Marina
2013-01-01
We propose an approach to quantum theory based on the energetic causal sets, introduced in Cort\\^{e}s and Smolin (2013). Fundamental processes are causal sets whose events carry momentum and energy, which are transmitted along causal links and conserved at each event. Fundamentally there are amplitudes for such causal processes, but no space-time. An embedding of the causal processes in an emergent space-time arises only at the semiclassical level. Hence, fundamentally there are no commutation relations, no uncertainty principle and, indeed, no hbar. All that remains of quantum theory is the relationship between the absolute value squared of complex amplitudes and probabilities. Consequently, we find that neither locality, nor non locality, are primary concepts, only causality exists at the fundamental level.
Louie, Jacob; Shalaby, Amer; Habib, Khandker Nurul
2017-01-01
Most investigations of incident-related delay duration in the transportation context are restricted to highway traffic, with little attention given to delays due to transit service disruptions. Studies of transit-based delay duration are also considerably less comprehensive than their highway counterparts with respect to examining the effects of non-causal variables on the delay duration. However, delays due to incidents in public transit service can have serious consequences on the overall urban transportation system due to the pivotal and vital role of public transit. The ability to predict the durations of various types of transit system incidents is indispensable for better management and mitigation of service disruptions. This paper presents a detailed investigation on incident delay durations in Toronto's subway system over the year 2013, focusing on the effects of the incidents' location and time, the train-type involved, and the non-adherence to proper recovery procedures. Accelerated Failure Time (AFT) hazard models are estimated to investigate the relationship between these factors and the resulting delay duration. The empirical investigation reveals that incident types that impact both safety and operations simultaneously generally have longer expected delays than incident types that impact either safety or operations alone. Incidents at interchange stations are cleared faster than incidents at non-interchange stations. Incidents during peak periods have nearly the same delay durations as off-peak incidents. The estimated models are believed to be useful tools in predicting the relative magnitude of incident delay duration for better management of subway operations.
Blaisdell, A.P.; Beckers, T.
2009-01-01
The article discusses various reports published within the issue, including one on psychological approaches to causal discovery in humans, one on the representational and reasoning capacities that underlie causal cognition in rats and one on the generality of knowledge of Great Ape.
Yeang, Calvin; Cotter, Bruno; Tsimikas, Sotirios
2016-02-01
Lipoprotein(a) [Lp(a)], comprised of apolipoprotein(a) [apo(a)] and a low-density lipoprotein-like particle, is a genetically determined, causal risk factor for cardiovascular disease and calcific aortic valve stenosis. Lp(a) is the major plasma lipoprotein carrier of oxidized phospholipids, is pro-inflammatory, inhibits plasminogen activation, and promotes smooth muscle cell proliferation, as defined mostly through in vitro studies. Although Lp(a) is not expressed in commonly studied laboratory animals, mouse and rabbit models transgenic for Lp(a) and apo(a) have been developed to address their pathogenicity in vivo. These models have provided significant insights into the pathophysiology of Lp(a), particularly in understanding the mechanisms of Lp(a) in mediating atherosclerosis. Studies in Lp(a)-transgenic mouse models have demonstrated that apo(a) is retained in atheromas and suggest that it promotes fatty streak formation. Furthermore, rabbit models have shown that Lp(a) promotes atherosclerosis and vascular calcification. However, many of these models have limitations. Mouse models need to be transgenic for both apo(a) and human apolipoprotein B-100 since apo(a) does not covalently associated with mouse apoB to form Lp(a). In established mouse and rabbit models of atherosclerosis, Lp(a) levels are low, generally model whereas over 40 isoforms exist in humans. Mouse models should also ideally be studied in an LDL receptor negative background for atherosclerosis studies, as mice don't develop sufficiently elevated plasma cholesterol to study atherosclerosis in detail. With recent data that cardiovascular disease and calcific aortic valve stenosis is causally mediated by the LPA gene, development of optimized Lp(a)-transgenic animal models will provide an opportunity to further understand the mechanistic role of Lp(a) in atherosclerosis and aortic stenosis and provide a platform to test novel therapies for cardiovascular disease.
Causality and contagion in peripheral EMU public debt markets: a dynamic approach
Gomez-Puig, Marta; Sosvilla Rivero, Simón Javier
2016-01-01
Nuestra investigación tiene como objetivo analizar las relaciones causales en el comportamiento de la deuda pública emitida por países miembros periféricos de la Unión Económica y Monetaria (UEM), con especial énfasis en los recientes episodios de crisis desatados en los mercados de deuda soberana de la zona euro desde 2009. Con este objetivo, empleamos una base de datos de la frecuencia diaria de los rendimientos de los bonos gubernamentales a 10 años emitidos por cinco países de la UEM (Gre...
The relationship of family characteristics and bipolar disorder using causal-pie models.
Chen, Y-C; Kao, C-F; Lu, M-K; Yang, Y-K; Liao, S-C; Jang, F-L; Chen, W J; Lu, R-B; Kuo, P-H
2014-01-01
Many family characteristics were reported to increase the risk of bipolar disorder (BPD). The development of BPD may be mediated through different pathways, involving diverse risk factor profiles. We evaluated the associations of family characteristics to build influential causal-pie models to estimate their contributions on the risk of developing BPD at the population level. We recruited 329 clinically diagnosed BPD patients and 202 healthy controls to collect information in parental psychopathology, parent-child relationship, and conflict within family. Other than logistic regression models, we applied causal-pie models to identify pathways involved with different family factors for BPD. The risk of BPD was significantly increased with parental depression, neurosis, anxiety, paternal substance use problems, and poor relationship with parents. Having a depressed mother further predicted early onset of BPD. Additionally, a greater risk for BPD was observed with higher numbers of paternal/maternal psychopathologies. Three significant risk profiles were identified for BPD, including paternal substance use problems (73.0%), maternal depression (17.6%), and through poor relationship with parents and conflict within the family (6.3%). Our findings demonstrate that different aspects of family characteristics elicit negative impacts on bipolar illness, which can be utilized to target specific factors to design and employ efficient intervention programs.
Darwin's diagram of divergence of taxa as a causal model for the origin of species.
Bouzat, Juan L
2014-03-01
On the basis that Darwin's theory of evolution encompasses two logically independent processes (common descent and natural selection), the only figure in On the Origin of Species (the Diagram of Divergence of Taxa) is often interpreted as illustrative of only one of these processes: the branching patterns representing common ancestry. Here, I argue that Darwin's Diagram of Divergence of Taxa represents a broad conceptual model of Darwin's theory, illustrating the causal efficacy of natural selection in producing well-defined varieties and ultimately species. The Tree Diagram encompasses the idea that natural selection explains common descent and the origin of organic diversity, thus representing a comprehensive model of Darwin's theory on the origin of species. I describe Darwin's Tree Diagram in relation to his argumentative strategy under the vera causa principle, and suggest that the testing of his theory based on the evidence from the geological record, the geographical distribution of organisms, and the mutual affinities of organic beings can be framed under the hypothetico-deductive method. Darwin's Diagram of Divergence of Taxa therefore represents a broad conceptual model that helps understanding the causal construction of Darwin's theory of evolution, the structure of his argumentative strategy, and the nature of his scientific methodology.
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 eqations 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. In particular, I apply this reasoning to Maxwell's equations, which is an instructive example since their casual properties are sometimes debated.
Causality, mathematical models and statistical association: dismantling evidence-based medicine.
Thompson, R Paul
2010-04-01
From humble beginnings, largely at the medical school at McMaster University, Canada, the evidence-based medicine (EBM) movement has enjoyed a spectacular rise in international acceptance over the last 25 years. Randomized controlled trials (RCTs) and systematic reviews based on them have pride of place (the gold standard) in EBM's hierarchy of evidence; models and theories are relegated to the bottom of the hierarchy. In the last decade, RCTs have been extensively criticized. I briefly rehearse those criticisms because they are an important backdrop to the criticism of EBM developed in this paper. In essence, the argument developed here is that RCTs use mathematics solely as a tool of analysis rather than as the language of the science and that this fundamentally affects the validity of causal claims. As EBM gives pride of place to RCTs and devalues theoretical models - a devaluation that would be incomprehensible to a physicist or biologist - the validity of EBM's causal claims and knowledge claims are weak and far from a 'gold standard'.
Futures Business Models for an IoT Enabled Healthcare Sector: A Causal Layered Analysis Perspective
Directory of Open Access Journals (Sweden)
Julius Francis Gomes
2016-12-01
Full Text Available Purpose: To facilitate futures business research by proposing a novel way to combine business models as a conceptual tool with futures research techniques. Design: A futures perspective is adopted to foresight business models of the Internet of Things (IoT enabled healthcare sector by using business models as a futures business research tool. In doing so, business models is coupled with one of the most prominent foresight methodologies, Causal Layered Analysis (CLA. Qualitative analysis provides deeper understanding of the phenomenon through the layers of CLA; litany, social causes, worldview and myth. Findings: It is di cult to predict the far future for a technology oriented sector like healthcare. This paper presents three scenarios for short-, medium- and long-term future. Based on these scenarios we also present a set of business model elements for different future time frames. This paper shows a way to combine business models with CLA, a foresight methodology; in order to apply business models in futures business research. Besides offering early results for futures business research, this study proposes a conceptual space to work with individual business models for managerial stakeholders. Originality / Value: Much research on business models has offered conceptualization of the phenomenon, innovation through business model and transformation of business models. However, existing literature does not o er much on using business model as a futures research tool. Enabled by futures thinking, we collected key business model elements and building blocks for the futures market and ana- lyzed them through the CLA framework.
Mangiarotti, S.; Sekhar, M.; Berthon, L.; Javeed, Y.; Mazzega, P.
2012-08-01
Causal relationships existing between observed levels of groundwater in a semi-arid sub-basin of the Kabini River basin (Karnataka state, India) are investigated in this study. A Vector Auto Regressive model is used for this purpose. Its structure is built on an upstream/downstream interaction network based on observed hydro-physical properties. Exogenous climatic forcing is used as an input based on cumulated rainfall departure. Optimal models are obtained thanks to a trial approach and are used as a proxy of the dynamics to derive causal networks. It appears to be an interesting tool for analysing the causal relationships existing inside the basin. The causal network reveals 3 main regions: the Northeastern part of the Gundal basin is closely coupled to the outlet dynamics. The Northwestern part is mainly controlled by the climatic forcing and only marginally linked to the outlet dynamic. Finally, the upper part of the basin plays as a forcing rather than a coupling with the lower part of the basin allowing for a separate analysis of this local behaviour. The analysis also reveals differential time scales at work inside the basin when comparing upstream oriented with downstream oriented causalities. In the upper part of the basin, time delays are close to 2 months in the upward direction and lower than 1 month in the downward direction. These time scales are likely to be good indicators of the hydraulic response time of the basin which is a parameter usually difficult to estimate practically. This suggests that, at the sub-basin scale, intra-annual time scales would be more relevant scales for analysing or modelling tropical basin dynamics in hard rock (granitic and gneissic) aquifers ubiquitous in south India.
Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains
Directory of Open Access Journals (Sweden)
Michael Krumin
2010-01-01
Full Text Available Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden’’ Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.
Effective coping with stroke disability in a community setting: the development of a causal model.
Boynton De Sepulveda, L I; Chang, B
1994-08-01
A proposed causal model based upon Lazarus' theory of psychological stress and coping was tested in a sample of 75 persons disabled by stroke. Coping constraints such as demographic and stroke factors were hypothesized to affect resources (perceived availability of social support, perceived effectiveness of social support, social contact), stress appraisal, coping behavior and coping effectiveness. Although the model did not fit the data, several path coefficients within the model were statistically significant. Functional status was positively related to resources and negatively related to the stressor. Resources were negatively related to the stressor and positively related to coping effectiveness. It was noted that the buffering effect of social support was related to the level of disability of the stroke person. Persons with functional disability following stroke also had decreased social contact, perceived less availability of social resources and increased threat to physical well-being, and had reduced coping effectiveness.
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...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
ARTS: A System-Level Framework for Modeling MPSoC Components and Analysis of their Causality
DEFF Research Database (Denmark)
Mahadevan, Shankar; Storgaard, Michael; Madsen, Jan;
2005-01-01
the MPSoC designers in modeling the different layers and understanding their causalities. While others have developed tools for static analysis and modeled limited correlations (processor-memory or processor-communication), our model captures the impact of dynamic and unpredictable OS behaviour...
Beaumelle, Léa; Vile, Denis; Lamy, Isabelle; Vandenbulcke, Franck; Gimbert, Frédéric; Hedde, Mickaël
2016-11-01
Structural equation models (SEM) are increasingly used in ecology as multivariate analysis that can represent theoretical variables and address complex sets of hypotheses. Here we demonstrate the interest of SEM in ecotoxicology, more precisely to test the three-step concept of metal bioavailability to earthworms. The SEM modeled the three-step causal chain between environmental availability, environmental bioavailability and toxicological bioavailability. In the model, each step is an unmeasured (latent) variable reflected by several observed variables. In an exposure experiment designed specifically to test this SEM for Cd, Pb and Zn, Aporrectodea caliginosa was exposed to 31 agricultural field-contaminated soils. Chemical and biological measurements used included CaC12-extractable metal concentrations in soils, free ion concentration in soil solution as predicted by a geochemical model, dissolved metal concentration as predicted by a semi-mechanistic model, internal metal concentrations in total earthworms and in subcellular fractions, and several biomarkers. The observations verified the causal definition of Cd and Pb bioavailability in the SEM, but not for Zn. Several indicators consistently reflected the hypothetical causal definition and could thus be pertinent measurements of Cd and Pb bioavailability to earthworm in field-contaminated soils. SEM highlights that the metals present in the soil solution and easily extractable are not the main source of available metals for earthworms. This study further highlights SEM as a powerful tool that can handle natural ecosystem complexity, thus participating to the paradigm change in ecotoxicology from a bottom-up to a top-down approach.
K-causality coincides with stable causality
Minguzzi, E
2008-01-01
It is proven that K-causality coincides with stable causality, and that in a K-causal spacetime the relation K^+ coincides with the Seifert's relation. As a consequence the causal relation "the spacetime is strongly causal and the closure of the causal relation is transitive" stays between stable causality and causal continuity.
Chacko, S B; Huba, M E
1991-06-01
This article tested relationships among variables depicted in a causal learning model of academic achievement developed by the authors. The Learning and Study Skills (LASSI), Life Experience Survey (LES), and ASSET test were administered to 134 first-semester nursing students at a 2-year community college. The path analysis supported 11 of the 14 pathways tested. Language ability, reading ability, and self-efficacy were found to be direct effects on academic achievement. When self-efficacy was the criterion, students' language ability, math ability, motivation, and concentration and preparation for class were direct effects. Life stress, motivation, and self-monitoring/use of study strategies were found to be direct effects on students' concentration and preparation for class. In turn, when the ability to self-monitor and use study strategies was the criterion, motivation was the only direct effect. Overall, the model explained 46% of the variance in academic achievement.
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.
The Epstein-Glaser causal approach to the Light-Front QED$_{4}$. I: Free theory
Bufalo, R; Soto, D E
2014-01-01
In this work we present the study of light-front field theories in the realm of axiomatic theory. It is known that when one uses the light-cone gauge pathological poles $\\left( k^{+}\\right) ^{-n}$ arises, demanding a prescription to be employed in order to tame these ill-defined poles and to have correct Feynman integrals due to the lack of Wick rotation in such theories. In order to shed a new light on this long standing problem we present here a discussion based on the use rigorous mathematical machinery of distributions combined with physical concepts, such as causality, to show how to deal with these singular propagators in a general fashion without making use of any prescription. The first step of our development will consist in showing how analytic representation for propagators arises by requiring general physical properties in the framework of Wightman's formalism. From that we shall determine the equal-time (anti)commutation relations in the light-front form for the scalar, fermionic fields and for t...
Identifying abnormal connectivity in patients using Dynamic Causal Modelling of fMRI responses.
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Mohamed L Seghier
2010-08-01
Full Text Available Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensori-motor and cognitive tasks can be carried out when parts of the neural system that support normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these inter-regional interactions deviate from normal. Dynamic Causal Modelling (DCM offers the opportunity to assess task-dependent interactions within a set of regions. Here we review its use in patients when the question of interest concerns the characterisation of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic nonlinear models with two-state equations. We also highlight the importance of the new Bayesian model selection and averaging tools that allow different plausible models to be compared at the single subject and group level. These procedures allow inferences to be made at different levels of model selection, from features (model families to connectivity parameters. Following a critical review of previous DCM studies that investigated abnormal connectivity we propose a systematic procedure that will ensure more flexibility and efficiency when using DCM in patients. Finally, some practical and methodological issues crucial for interpreting or generalising DCM findings in patients are discussed.
Tighe, Elizabeth L.; Wagner, Richard K.; Schatschneider, Christopher
2015-01-01
This study demonstrates the utility of applying a causal indicator modeling framework to investigate important predictors of reading comprehension in third, seventh, and tenth grade students. The results indicated that a 4-factor multiple indicator multiple indicator cause (MIMIC) model of reading comprehension provided adequate fit at each grade…
The Temporal Logic of Causal Structures
Kleinberg, Samantha
2012-01-01
Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal f...
Usami, Satoshi; Hayes, Timothy; McArdle, John J
2015-01-01
The present paper focuses on the relationship between latent change score (LCS) and autoregressive cross-lagged (ARCL) factor models in longitudinal designs. These models originated from different theoretical traditions for different analytic purposes, yet they share similar mathematical forms. In this paper, we elucidate the mathematical relationship between these models and show that the LCS model is reduced to the ARCL model when fixed effects are assumed in the slope factor scores. Additionally, we provide an applied example using height and weight data from a gerontological study. Throughout the example, we emphasize caution in choosing which model (ARCL or LCS) to apply due to the risk of obtaining misleading results concerning the presence and direction of causal precedence between two variables. We suggest approaching model specification not only by comparing estimates and fit indices between the LCS and ARCL models (as well as other models) but also by giving appropriate weight to substantive and theoretical considerations, such as assessing the justifiability of the assumption of random effects in the slope factor scores.
Causal and causally separable processes
Oreshkov, Ognyan; Giarmatzi, Christina
2016-09-01
The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and
Bönstrup, Marlene; Schulz, Robert; Feldheim, Jan; Hummel, Friedhelm C; Gerloff, Christian
2016-01-01
Dynamic causal modelling (DCM) has extended the understanding of brain network dynamics in a variety of functional systems. In the motor system, DCM studies based on functional magnetic resonance imaging (fMRI) or on magneto-/electroencephalography (M/EEG) have demonstrated movement-related causal information flow from secondary to primary motor areas and have provided evidence for nonlinear cross-frequency interactions among motor areas. The present study sought to investigate to what extent fMRI- and EEG-based DCM might provide complementary and synergistic insights into neuronal network dynamics. Both modalities share principal similarities in the formulation of the DCM. Thus, we hypothesized that DCM based on induced EEG responses (DCM-IR) and on fMRI would reveal congruent task-dependent network dynamics. Brain electrical (63-channel surface EEG) and Blood Oxygenation Level Dependent (BOLD) signals were recorded in separate sessions from 14 healthy participants performing simple isometric right and left hand grips. DCM-IR and DCM-fMRI were used to estimate coupling parameters modulated by right and left hand grips within a core motor network of six regions comprising bilateral primary motor cortex (M1), ventral premotor cortex (PMv) and supplementary motor area (SMA). We found that DCM-fMRI and DCM-IR similarly revealed significant grip-related increases in facilitatory coupling between SMA and M1 contralateral to the active hand. A grip-dependent interhemispheric reciprocal inhibition between M1 bilaterally was only revealed by DCM-fMRI but not by DCM-IR. Frequency-resolved coupling analysis showed that the information flow from contralateral SMA to M1 was predominantly a linear alpha-to-alpha (9-13Hz) interaction. We also detected some cross-frequency coupling from SMA to contralateral M1, i.e., between lower beta (14-21Hz) at the SMA and higher beta (22-30Hz) at M1 during right hand grip and between alpha (9-13Hz) at SMA and lower beta (14-21Hz) at M1
Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.
2015-01-01
Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of
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...
Paynter, Stuart
2016-03-15
Conventional measures of causality (which compare risks between exposed and unexposed individuals) do not factor in the population-scale dynamics of infectious disease transmission. We used mathematical models of 2 childhood infections (respiratory syncytial virus and rotavirus) to illustrate this problem. These models incorporated 3 causal pathways whereby malnutrition could act to increase the incidence of severe infection: increasing the proportion of infected children who develop severe infection, increasing the children's susceptibility to infection, and increasing infectiousness. For risk factors that increased the proportion of infected children who developed severe infection, the population attributable fraction (PAF) calculated conventionally was the same as the PAF calculated directly from the models. However, for risk factors that increased transmission (by either increasing susceptibility to infection or increasing infectiousness), the PAF calculated directly from the models was much larger than that predicted by the conventional PAF calculation. The models also showed that even when conventional studies find no association between a risk factor and an outcome, risk factors that increase transmission can still have a large impact on disease burden. For a complete picture of infectious disease causality, transmission effects must be incorporated into causal models.
DEFF Research Database (Denmark)
Nielsen, Max; Jensen, Frank; Setälä, Jari;
2011-01-01
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...... 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...... 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...
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Directory of Open Access Journals (Sweden)
Miljana Valdec
2015-03-01
Full Text Available This paper contributes to the literature by using propensity score matching to test for causal effects of starting to export on firm performance in Croatian manufacturing firm-level data. The results confirm that exporters have characteristics superior to those of non-exporters. In the main sample specification there is pervasive evidence of self-selection into export markets, meaning that firms are successful years before they become exporters. Using multiple firm performance indicators, panel and cross section data models together with various sample specifications there is scant evidence on learning-by-exporting which holds true only in a few cases. On the other hand, higher sales growth is found to be a more conclusive distinguishing characteristic of new exporters. As in similar studies, we find that a part of the results depends on the number of export starters in the estimation sample.
Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling.
Cooray, Gerald K; Sengupta, Biswa; Douglas, Pamela; Englund, Marita; Wickstrom, Ronny; Friston, Karl
2015-09-01
We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory-inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis.
Yu, Wen; Chen, Kani; Sobel, Michael E; Ying, Zhiliang
2015-03-01
We consider causal inference in randomized survival studies with right censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditional on covariates and latent compliance type. Estimands depending on these distributions, for example, the complier average causal effect (CACE), the complier effect on survival beyond time t, and the complier quantile effect are then considered. Maximum likelihood is used to estimate the parameters of the transformation models, using a specially designed expectation-maximization (EM) algorithm to overcome the computational difficulties created by the mixture structure of the problem and the infinite dimensional parameter in the transformation models. The estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Inferential procedures for the causal parameters are developed. A simulation study is conducted to evaluate the finite sample performance of the estimated causal parameters. We also apply our methodology to a randomized study conducted by the Health Insurance Plan of Greater New York to assess the reduction in breast cancer mortality due to screening.
Setyaningsih, S.
2017-01-01
The main element to build a leading university requires lecturer commitment in a professional manner. Commitment is measured through willpower, loyalty, pride, loyalty, and integrity as a professional lecturer. A total of 135 from 337 university lecturers were sampled to collect data. Data were analyzed using validity and reliability test and multiple linear regression. Many studies have found a link on the commitment of lecturers, but the basic cause of the causal relationship is generally neglected. These results indicate that the professional commitment of lecturers affected by variables empowerment, academic culture, and trust. The relationship model between variables is composed of three substructures. The first substructure consists of endogenous variables professional commitment and exogenous three variables, namely the academic culture, empowerment and trust, as well as residue variable ɛ y . The second substructure consists of one endogenous variable that is trust and two exogenous variables, namely empowerment and academic culture and the residue variable ɛ 3. The third substructure consists of one endogenous variable, namely the academic culture and exogenous variables, namely empowerment as well as residue variable ɛ 2. Multiple linear regression was used in the path model for each substructure. The results showed that the hypothesis has been proved and these findings provide empirical evidence that increasing the variables will have an impact on increasing the professional commitment of the lecturers.
Infertile Individuals’ Marital Relationship Status, Happiness, and Mental Health: A Causal Model
Directory of Open Access Journals (Sweden)
Seyed Habiballah Ahmadi Forooshany
2014-11-01
Full Text Available Background: This study examined the causal model of relation between marital relationship status, happiness, and mental health in infertile individuals. Materials and Methods: In this descriptive study, 155 subjects (men: 52 and women: 78, who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Questionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Results: Disregarding the gender factor, marital relationship status was directly related to happiness (p<0.05 and happiness was directly related to mental health, (p<0.05. Also, indirect relation between marital relationship status and mental health was significant (p<0.05. These results were confirmed in women participants but in men participants only the direct relation between happiness and mental health was significant (p<0.05. Conclusion: Based on goodness of model fit in fitness indexes, happiness had a mediator role in relation between marital relationship status and mental health in infertile individuals disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health.
Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.
Cooray, Gerald K; Sengupta, Biswa; Douglas, Pamela K; Friston, Karl
2016-01-15
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5-10min compared to approximately 1-2h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated.
A meta-frontier approach for causal inference in productivity analysis
DEFF Research Database (Denmark)
Henningsen, Arne; Mpeta, Daniel F.; Adem, Anwar S.
use the approach of Bravo-Ureta, Greene and Solís (2012) to estimate two separate production frontiers (one for contract farmers and one for non-contract farmers) that account for potential biases due to self-selection on both observed and unobserved variables. Then, we follow Rao, Brümmer and Qaim...... by the contractor’s provision of (additional) extension service and seeds of high-yielding varieties to the contract farmers....
Ishanu Chattopadhyay
2014-01-01
While correlation measures are used to discern statistical relationships between observed variables in almost all branches of data-driven scientific inquiry, what we are really interested in is the existence of causal dependence. Designing an efficient causality test, that may be carried out in the absence of restrictive pre-suppositions on the underlying dynamical structure of the data at hand, is non-trivial. Nevertheless, ability to computationally infer statistical prima facie evidence of...
Estimating the Effects of Obesity and Weight Change on Mortality Using a Dynamic Causal Model
Bochen Cao
2015-01-01
Background A well-known challenge in estimating the mortality risks of obesity is reverse causality attributable to illness-associated and smoking-associated weight loss. Given that the likelihood of chronic and acute illnesses rises with age, reverse causality is most threatening to estimates derived from elderly populations. Methods I analyzed data from 12,523 respondents over 50 years old from a nationally representative longitudinal dataset, the Health and Retirement Study (HRS). The effe...
Implementing causality in the spin foam quantum geometry
Livine, E R; Livine, Etera R.; Oriti, Daniele
2003-01-01
We analyse the classical and quantum geometry of the Barrett-Crane spin foam model for four dimensional quantum gravity, explaining why it has to be considering as a covariant realization of the projector operator onto physical quantum gravity states. We discuss how causality requirements can be consistently implemented in this framework, and construct causal transiton amplitudes between quantum gravity states, i.e. realising in the spin foam context the Feynman propagator between states. The resulting causal spin foam model can be seen as a path integral quantization of Lorentzian first order Regge calculus, and represents a link between several approaches to quantum gravity as canonical loop quantum gravity, sum-over-histories formulations, dynamical triangulations and causal sets. In particular, we show how the resulting model can be rephrased within the framework of quantum causal sets (or histories).
Correlation Measure Equivalence in Dynamic Causal Structures
Gyongyosi, Laszlo
2016-01-01
We prove an equivalence transformation between the correlation measure functions of the causally-unbiased quantum gravity space and the causally-biased standard space. The theory of quantum gravity fuses the dynamic (nonfixed) causal structure of general relativity and the quantum uncertainty of quantum mechanics. In a quantum gravity space, the events are causally nonseparable and all time bias vanishes, which makes it no possible to use the standard causally-biased entropy and the correlation measure functions. Since a corrected causally-unbiased entropy function leads to an undefined, obscure mathematical structure, in our approach the correction is made in the data representation of the causally-unbiased space. We prove that the standard causally-biased entropy function with a data correction can be used to identify correlations in dynamic causal structures. As a corollary, all mathematical properties of the causally-biased correlation measure functions are preserved in the causally-unbiased space. The eq...
On modeling HIV and T cells in vivo: assessing causal estimators in vaccine trials.
Directory of Open Access Journals (Sweden)
W David Wick
2006-06-01
Full Text Available The first efficacy trials--named STEP--of a T cell vaccine against HIV/AIDS began in 2004. The unprecedented structure of these trials raised new modeling and statistical challenges. Is it plausible that memory T cells, as opposed to antibodies, can actually prevent infection? If they fail at prevention, to what extent can they ameliorate disease? And how do we estimate efficacy in a vaccine trial with two primary endpoints, one traditional, one entirely novel (viral load after infection, and where the latter may be influenced by selection bias due to the former? In preparation for the STEP trials, biostatisticians developed novel techniques for estimating a causal effect of a vaccine on viral load, while accounting for post-randomization selection bias. But these techniques have not been tested in biologically plausible scenarios. We introduce new stochastic models of T cell and HIV kinetics, making use of new estimates of the rate that cytotoxic T lymphocytes--CTLs; the so-called killer T cells--can kill HIV-infected cells. Based on these models, we make the surprising discovery that it is not entirely implausible that HIV-specific CTLs might prevent infection--as the designers explicitly acknowledged when they chose the endpoints of the STEP trials. By simulating thousands of trials, we demonstrate that the new statistical methods can correctly identify an efficacious vaccine, while protecting against a false conclusion that the vaccine exacerbates disease. In addition to uncovering a surprising immunological scenario, our results illustrate the utility of mechanistic modeling in biostatistics.
Revisiting Causality in Markov Chains
Shojaee, Abbas
2016-01-01
Identifying causal relationships is a key premise of scientific research. The growth of observational data in different disciplines along with the availability of machine learning methods offers the possibility of using an empirical approach to identifying potential causal relationships, to deepen our understandings of causal behavior and to build theories accordingly. Conventional methods of causality inference from observational data require a considerable length of time series data to capture cause-effect relationship. We find that potential causal relationships can be inferred from the composition of one step transition rates to and from an event. Also known as Markov chain, one step transition rates are a commonly available resource in different scientific disciplines. Here we introduce a simple, effective and computationally efficient method that we termed 'Causality Inference using Composition of Transitions CICT' to reveal causal structure with high accuracy. We characterize the differences in causes,...
Chen, Mei-Chih; Chang, Kaowen
2014-11-06
Many city governments choose to supply more developable land and transportation infrastructure with the hope of attracting people and businesses to their cities. However, like those in Taiwan, major cities worldwide suffer from traffic congestion. This study applies the system thinking logic of the causal loops diagram (CLD) model in the System Dynamics (SD) approach to analyze the issue of traffic congestion and other issues related to roads and land development in Taiwan's cities. Comparing the characteristics of development trends with yearbook data for 2002 to 2013 for all of Taiwan's cities, this study explores the developing phenomenon of unlimited city sprawl and identifies the cause and effect relationships in the characteristics of development trends in traffic congestion, high-density population aggregation in cities, land development, and green land disappearance resulting from city sprawl. This study provides conclusions for Taiwan's cities' sustainability and development (S&D). When developing S&D policies, during decision making processes concerning city planning and land use management, governments should think with a holistic view of carrying capacity with the assistance of system thinking to clarify the prejudices in favor of the unlimited developing phenomena resulting from city sprawl.
Hellaby, C; Hellaby, Charles; Krasinski, Andrzej
2002-01-01
The spherically symmetric dust model of Lemaitre-Tolman can describe wormholes, but the causal communication between the two asymptotic regions through the neck is even less than in the vacuum (Schwarzschild-Kruskal-Szekeres) case. We investigate the anisotropic generalisation of the wormhole topology in the Szekeres model. The function E(r, p, q) describes the deviation from spherical symmetry if \\partial_r E \
Thomas, R
2006-07-01
The problem of disentangling complex dynamic systems is addressed, especially with a view to identifying those variables that take part in the essential qualitative behaviour of systems. The author presents a series of reflections about the methods of formalisation together with the principles that govern the global operation of systems. In particular, a section on circuits, nuclei, and circular causality and a rather detailed description of the analytic use of the generalised asynchronous logical description, together with a brief description of its synthetic use (OreverseO logic). Some basic rules are recalled, such as the fact that a positive circuit is a necessary condition of multistationarity. Also, the interest of considering as a model, rather than a well-defined set of differential equations, a variety of systems that differ from each other only by the values of constant terms is emphasised. All these systems have a common Jacobian matrix and for all of them phase space has exactly the same structure. It means that all can be partitioned in the same way as regards the signs of the eigenvalues and thus as regards the precise nature of any steady states that might be present. Which steady states are actually present, depends on the values of terms of order zero in the ordinary differential equations (ODEs), and it is easy to find for which values of these terms a given point in phase space is steady. Models can be synthesised first at the level of the circuits involved in the Jacobian matrix (that determines which types and numbers of steady states are consistent with the model), then only at the level of terms of order zero in the ODE's (that determines which of the steady states actually exist), hence the title 'Circular casuality'.
Immirzi, Giorgio
2016-01-01
I discuss how to impose causality on spin-foam models, separating forward and backward propagation, turning a given triangulation to a 'causal set', and giving asymptotically the exponential of the Regge action, not a cosine. I show the equivalence of the prescriptions which have been proposed to achieve this. Essential to the argument is the closure condition for the 4-simplices, all made of space-like tetrahedra.
DEFF Research Database (Denmark)
Kogelman, Lisette; Zhernakova, Daria V.; Westra, Harm-Jan;
2015-01-01
expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected...... polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. CONCLUSIONS: To our knowledge......BACKGROUND: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about...
Noreika, Valdas; Gueorguiev, David; Shtyrov, Yury; Bekinschtein, Tristan A.; Henson, Richard
2016-01-01
There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called “mismatch response”). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an “omission” response). This situation arguably provides a more direct measure of “top-down” predictions in the absence of confounding “bottom-up” input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of “bottom-up” stimuli with the presence versus absence of “top-down” attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward “prediction” connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. SIGNIFICANCE STATEMENT Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known “mismatch response.” But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain
Material Modelling - Composite Approach
DEFF Research Database (Denmark)
Nielsen, Lauge Fuglsang
1997-01-01
, and internal stresses caused by drying shrinkage with experimental results reported in the literature on the mechanical behavior of mature concretes. It is then concluded that the model presented applied in general with respect to age at loading.From a stress analysis point of view the most important finding......This report is part of a research project on "Control of Early Age Cracking" - which, in turn, is part of the major research programme, "High Performance Concrete - The Contractor's Technology (HETEK)", coordinated by the Danish Road Directorate, Copenhagen, Denmark, 1997.A composite......-rheological model of concrete is presented by which consistent predictions of creep, relaxation, and internal stresses can be made from known concrete composition, age at loading, and climatic conditions. No other existing "creep prediction method" offers these possibilities in one approach.The model...
非线性因果模型辨识方法%IDENTIFICATION METHOD FOR NONLINEAR CAUSAL MODELS
Institute of Scientific and Technical Information of China (English)
姜枫; 周莉莉
2015-01-01
近来，基于观测变量的因果模型辨识受到了较多关注。一般使用线性无环因果模型对数据生成过程建模，而实际上，许多因果模型包含非线性关系，使用纯线性方法求解是无效的。将线性模型泛化为非线性模型，提出一种两步骤的辨识算法，首先使用特征选择算法获得d分离等价类，然后使用非线性成对独立性测试为图中的边标注因果方向。实验结果验证了该算法的有效性，并表明其优于其他算法。%The identification of causal models based on observed variables has received much attention in the past.Linear acyclic causal models are usually used to model the data-generating process,but practically many causal relationships are more or less nonlinear,this raises the doubts to the usefulness of purely linear methods.In this paper,we generalise the basic linear model to nonlinear model,and propose a two-step identification method,which first uses feature-selection algorithm to obtain the d-separation equivalence class,and then uses nonlinear pairwise independence tests to mark the causal directions for edges in the image.Experimental results verify the validity of this algorithm and show that it outperforms other methods.
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.
Research Survey of Dynamic Causal Models%动态因果模型的研究综述
Institute of Scientific and Technical Information of China (English)
邓红霞; 游雅; 李海芳
2013-01-01
With the development of functional magneticresonance imaging technology has laid a foundation for revealing the mechanisms of interval brain effective connection, dynamic causal model will be more conducive to the study of the connection mechanism, which is effective and direct method to reveal the mysteries of the brain. This paper summarized the basic concepts and principles of dynamic causal model, discussed the connection modeand method of thedifferent of dynamic causal model, analyzed the distinction between the different classes of models, distinguished model using Bayesian model selection. Through summarizing the experiment of predecessors, drawed that dynamic causal model should follow the rules, generalized the existing problem. This paper also presented a summary of the current art of the state of Dynamic causal model, a discussion on the future researches topics and some crucial problems which should be solved pressingly.%功能磁共振成像技术的发展为揭示脑区间的有效连接机制奠定了基础，而动态因果模型的研究将更有利于连接机制的研究，为揭示脑的奥秘提供了有效、直接的方法。阐述了动态因果模型的基本概念和原理，论述了不同类别的动态因果模型连接方式、方法；分析了不同类别模型间的区别，并通过贝叶斯模型选择进行模型辨识。通过总结前人所做的工作，得出动态因果模型在使用过程中应该遵循的规则，概括了存在的问题。结合已有的动态因果模型研究成果，展望了未来的研究方向和亟待解决的关键问题。
Aging into perceptual control: A Dynamic Causal Modeling for fMRI study of bistable perception
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Ehsan eDowlati
2016-03-01
Full Text Available Aging is accompanied by stereotyped changes in functional brain activations, for example a cortical shift in activity patterns from posterior to anterior regions is one hallmark revealed by functional magnetic resonance imaging (fMRI of aging cognition. Whether these neuronal effects of aging could potentially contribute to an amelioration of or resistance to the cognitive symptoms associated with psychopathology remains to be explored. We used a visual illusion paradigm to address whether aging affects the cortical control of perceptual beliefs and biases. Our aim was to understand the effective connectivity associated with volitional control of ambiguous visual stimuli and to test whether greater top-down control of early visual networks emerged with advancing age. Using a bias training paradigm for ambiguous images we found that older participants (n = 16 resisted experimenter-induced visual bias compared to a younger cohort (n = 14 and that this resistance was associated with greater activity in prefrontal and temporal cortices. By applying Dynamic Causal Models for fMRI we uncovered a selective recruitment of top-down connections from the middle temporal to lingual gyrus by the older cohort during the perceptual switch decision following bias training. In contrast, our younger cohort did not exhibit any consistent connectivity effects but instead showed a loss of driving inputs to orbitofrontal sources following training. These findings suggest that perceptual beliefs are more readily controlled by top-down strategies in older adults and introduce age-dependent neural mechanisms that may be important for understanding aberrant belief states associated with psychopathology.
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Philippe eAlbouy
2015-02-01
Full Text Available Congenital amusia is a neuro-developmental disorder that primarily manifests as a difficulty in the perception and memory of pitch-based materials, including music. Recent findings have shown that the amusic brain exhibits altered functioning of a fronto-temporal network during pitch perception and memory. Within this network, during the encoding of melodies, a decreased right backward frontal-to-temporal connectivity was reported in amusia, along with an abnormal connectivity within and between auditory cortices. The present study investigated whether connectivity patterns between these regions were affected during the retrieval of melodies. Amusics and controls had to indicate whether sequences of six tones that were presented in pairs were the same or different. When melodies were different only one tone changed in the second melody. Brain responses to the changed tone in Different trials and to its equivalent (original tone in Same trials were compared between groups using Dynamic Causal Modeling (DCM. DCM results confirmed that congenital amusia is characterized by an altered effective connectivity within and between the two auditory cortices during sound processing. Furthermore, right temporal-to-frontal message passing was altered in comparison to controls, with an increase in Same trials and a decrease in Different trials. An additional analysis in control participants emphasized that the detection of an unexpected event in the typically functioning brain is supported by right fronto-temporal connections. The results can be interpreted in a predictive coding framework as reflecting an abnormal prediction error sent by temporal auditory regions towards frontal areas in the amusic brain.
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Liangsuo Ma
2015-01-01
Full Text Available Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD subjects studied with functional magnetic resonance imaging (fMRI. However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM to study the effective (directional neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard. The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal–striatal connectivity was modulated (influenced during the NoGo conditions for both groups. The effective connectivity from left (L anterior cingulate cortex (ACC to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R dorsolateral prefrontal cortex (DLPFC to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition.
Sharaev, Maksim G; Zavyalova, Viktoria V; Ushakov, Vadim L; Kartashov, Sergey I; Velichkovsky, Boris M
2016-01-01
The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.
Johnston, Steven
2010-01-01
Causal set theory provides a model of discrete spacetime in which spacetime events are represented by elements of a causal set---a locally finite, partially ordered set in which the partial order represents the causal relationships between events. The work presented here describes a model for matter on a causal set, specifically a theory of quantum scalar fields on a causal set spacetime background. The work starts with a discrete path integral model for particles on a causal set. Here quantum mechanical amplitudes are assigned to trajectories within the causal set. By summing these over all trajectories between two spacetime events we obtain a causal set particle propagator. With a suitable choice of amplitudes this is shown to agree (in an appropriate sense) with the retarded propagator for the Klein-Gordon equation in Minkowski spacetime. This causal set propagator is then used to define a causal set analogue of the Pauli-Jordan function that appears in continuum quantum field theories. A quantum scalar fi...
Chi, Do Minh
2001-01-01
We advance a famous principle - causality principle - but under a new view. This principle is a principium automatically leading to most fundamental laws of the nature. It is the inner origin of variation, rules evolutionary processes of things, and the answer of the quest for ultimate theories of the Universe.
Dynamic panel data models and causality : Applications to labor supply, health and insurance
Michaud, P.C.
2005-01-01
One of the main findings concerns the importance of common persistent factors, or unobserved traits of respondents, in order to study dynamic relationships between two variables of interest using panel data. The ¿hand of the past¿ can reinforce existent causal relationships, or blur their effect, po
A Program for Standard Errors of Indirect Effects in Recursive Causal Models.
Wolfle, Lee M.; Ethington, Corinna A.
In his early exposition of path analysis, Duncan (1966) noted that the method "provides a calculus for indirect effects." Despite the interest in indirect causal effects, most users treat them as if they are population parameters and do not test whether they are statistically significant. Sobel (1982) has recently derived the asymptotic…
Experimental test of nonlocal causality.
Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro
2016-08-01
Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect.
Experimental test of nonlocal causality
Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro
2016-01-01
Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045
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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
Ray, Suchismita; Haney, Margaret; Hanson, Catherine; Biswal, Bharat; Hanson, Stephen José
2015-12-01
The cues associated with drugs of abuse have an essential role in perpetuating problematic use, yet effective connectivity or the causal interaction between brain regions mediating the processing of drug cues has not been defined. The aim of this fMRI study was to model the causal interaction between brain regions within the drug-cue processing network in chronic cocaine smokers and matched control participants during a cocaine-cue exposure task. Specifically, cocaine-smoking (15M; 5F) and healthy control (13M; 4F) participants viewed cocaine and neutral cues while in the scanner (a Siemens 3 T magnet). We examined whole brain activation, including activation related to drug-cue processing. Time series data extracted from ROIs determined through our General Linear Model (GLM) analysis and prior publications were used as input to IMaGES, a computationally powerful Bayesian search algorithm. During cocaine-cue exposure, cocaine users showed a particular feed-forward effective connectivity pattern between the ROIs of the drug-cue processing network (amygdala → hippocampus → dorsal striatum → insula → medial frontal cortex, dorsolateral prefrontal cortex, anterior cingulate cortex) that was not present when the controls viewed the cocaine cues. Cocaine craving ratings positively correlated with the strength of the causal influence of the insula on the dorsolateral prefrontal cortex in cocaine users. This study is the first demonstration of a causal interaction between ROIs within the drug-cue processing network in cocaine users. This study provides insight into the mechanism underlying continued substance use and has implications for monitoring treatment response.
Human causal discovery from observational data.
1996-01-01
Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the poten...
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Túlio Aguiar
2003-12-01
Full Text Available Neste artigo, examinamos o aspecto assimétrico da relação causal, confrontando-o com o ponto de vista humiano e neo-humiano. Seguindo Hausman e Ehring, favorecemos uma abordagem situacional para a assimetria causal. Nós exploramos a análise do famoso exemplo do mastro (Flagpole, esclarecendo as conexões entre causação e explicação. Nosso diagnóstico geral é que a tradição neo-humiana supõe, equivocadamente, que as relações nômicas, com exceção de pequenos detalhes, exaurem as relações causais.This paper examines the asymmetrical aspect of causal relation, confronting it to Humean and Neo-Humean's view. Following Hausman and Ehring, we favor a situational approach to causal asymmetry. We explore the Hausman's analysis of flagpole's example, clearing the connexions between causation and explanation. Our general diagnosis is that the Neo-humean tradition wrongly supposes that nomic relations, with the exception of minor details, exhaust the causal relations.
Oriti, D
2004-01-01
We discuss the notion of causality in Quantum Gravity in the context of sum-over-histories approaches, in the absence therefore of any background time parameter. In the spin foam formulation of Quantum Gravity, we identify the appropriate causal structure in the orientation of the spin foam 2-complex and the data that characterize it; we construct a generalised version of spin foam models introducing an extra variable with the interpretation of proper time and show that different ranges of integration for this proper time give two separate classes of spin foam models: one corresponds to the spin foam models currently studied, that are independent of the underlying orientation/causal structure and are therefore interpreted as a-causal transition amplitudes; the second corresponds to a general definition of causal or orientation dependent spin foam models, interpreted as causal transition amplitudes or as the Quantum Gravity analogue of the Feynman propagator of field theory, implying a notion of ''timeless ord...
Fuertes Casals, Alba; Casals Casanova, Miquel; Gangolells Solanellas, Marta; Forcada Matheu, Núria; Macarulla Martí, Marcel; Roca Ramon, Xavier
2013-01-01
Despite the increasing efforts made by the construction sector to reduce the environmental impact of their processes, construction sites are still a major source of pollution and adverse impacts on the environment. This paper aims to improve the understanding of construction-related environmental impacts by identifying on-site causal factors and associated immediate circumstances during construc- tion processes for residential building projects. Based on the literature and focus g...
A New Life-Span Approach to Conscientiousness and Health: Combining the Pieces of the Causal Puzzle
Friedman, Howard S.; Kern, Margaret L.; Hampson, Sarah E.; Duckworth, Angela Lee
2014-01-01
Conscientiousness has been shown to predict healthy behaviors, healthy social relationships, and physical health and longevity. The causal links, however, are complex and not well elaborated. Many extant studies have used comparable measures for conscientiousness, and a systematic endeavor to build cross-study analyses for conscientiousness and…
Chu, X.; Wu, C.; Qiu, J.
2016-01-01
In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns
Temporal Causal Bayesian Network Model for Fault Diagnosis%故障诊断的时间因果贝叶斯网模型
Institute of Scientific and Technical Information of China (English)
周曙; 王晓茹; 钱清泉
2011-01-01
The alarms observed, the sequence of these alarms and the intervals between these alarms are critical factors in a certain type of fault diagnosis specially based on alarms of timing characteristics. In this paper a temporal causal Bayesian network model is proposed for this kind of fault diagnosis. The fuzzy method is used to discretize the temporal causal relations between faults and alarms, fuzzy operation is used to combine these temporal causal relations, and the fault hypothesis with the maximum likelihood is obtained by probability calculation. The theory and example demonstrate that this approach is correct and feasible.%在一类诊断所依据的可观信号(警报)具有时序特性的故障诊断问题中,诊断所依据的警报、这些警报出现的先后顺序以及它们之间的时间间隔,都与诊断结果有关联.针对这一类故障诊断问题,提出了时间因果贝叶斯网模型,采用模糊方式对故障与警报之间的时间因果关系进行离散化处理,用模糊运算来合成多个时间因果关系,通过概率计算获得最大可能的故障假说.理论与算例表明该方法有效可行.
Jennings, Wesley G; Park, MiRang; Richards, Tara N; Tomsich, Elizabeth; Gover, Angela; Powers, Ráchael A
2014-12-01
Child maltreatment is one of the most commonly examined risk factors for violence in dating relationships. Often referred to as the intergenerational transmission of violence or cycle of violence, a fair amount of research suggests that experiencing abuse during childhood significantly increases the likelihood of involvement in violent relationships later, but these conclusions are primarily based on correlational research designs. Furthermore, the majority of research linking childhood maltreatment and dating violence has focused on samples of young people from the United States. Considering these limitations, the current study uses a rigorous, propensity score matching approach to estimate the causal effect of experiencing child physical abuse on adult dating violence among a large sample of South Korean emerging adults. Results indicate that the link between child physical abuse and adult dating violence is spurious rather than causal. Study limitations and implications are discussed.
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
Di, Xin; Biswal, Bharat B
2014-02-01
The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on a resting-state fMRI data set to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01-0.08Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model wherein the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight into the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data.
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…
Ushakov, Vadim; Sharaev, Maksim G.; Kartashov, Sergey I.; Zavyalova, Viktoria V.; Verkhlyutov, Vitaliy M.; Velichkovsky, Boris M.
2016-01-01
The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively) within the default mode network (DMN) as represented by its key structures: the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and the inferior parietal cortex of left (LIPC) and right (RIPC) hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI) data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM). Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC, and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of effective
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Vadim Leonidovich Ushakov
2016-10-01
Full Text Available The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively within the default mode network (DMN as represented by its key structures: the medial prefrontal cortex (MPFC, posterior cingulate cortex (PCC and the inferior parietal cortex of left (LIPC and right (RIPC hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM. Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of
Sinha, Shriprakash
2016-12-01
Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid biologists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian network toolbox. The manuscript elucidates the coding contents of the advance article by Sinha (Integr. Biol. 6:1034-1048, 2014) and takes the reader in a step-by-step process of how (a) the collection and the transformation of the available biological information from literature is done, (b) the integration of the heterogeneous data and prior biological knowledge in the network is achieved, (c) the simulation study is designed, (d) the hypothesis regarding a biological phenomena is transformed into computational framework, and (e) results and inferences drawn using d-connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands-on experience of a perturbation project. Description of Matlab files is made available under GNU GPL v3 license at the Google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathway and https: //sites.google.com/site/shriprakashsinha/shriprakashsinha/projects/static-bn-for-wnt-signaling-pathway. Latest updates can be found in the latter website.
Kaufmann, Stefan
2013-08-01
The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals.
Decomposing Granger Causality over the Spectrum
A. Lemmens (Aurélie); C. Croux (Christophe); M.G. Dekimpe (Marnik)
2004-01-01
textabstractWe develop a bivariate spectral Granger-causality test that can be applied at each individual frequency of the spectrum. The spectral approach to Granger causality has the distinct advantage that it allows to disentangle (potentially) di®erent Granger- causality relationships over di®ere
Towards the Accuracy of Cybernetic Strategy Planning Models: Causal Proof and Function Approximation
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Christian A. Hillbrand
2003-04-01
Full Text Available All kind of strategic tasks within an enterprise require a deep understanding of its critical key success factors and their interrelations as well as an in-depth analysis of relevant environmental influences. Due to the openness of the underlying system, there seems to be an indefinite number of unknown variables influencing strategic goals. Cybernetic or systemic planning techniques try to overcome this intricacy by modeling the most important cause-and-effect relations within such a system. Although it seems to be obvious that there are specific influences between business variables, it is mostly impossible to identify the functional dependencies underlying such relations. Hence simulation or evaluation techniques based on such hypothetically assumed models deliver inaccurate results or fail completely. This paper addresses the need for accurate strategy planning models and proposes an approach to prove their cause-andeffect relations by empirical evidence. Based on this foundation an approach for the approximation of the underlying cause-andeffect function by the means of Artificial Neural Networks is developed.
Zigzagging causality EPR model: answer to Vigier and coworkers and to Sutherland
Energy Technology Data Exchange (ETDEWEB)
de Beauregard, O.C.
1987-08-01
The concept of propagation in time of Vigier and co-workers (V et al.) implies the ideal of a supertime; it is thus alien to most Minkowskian pictures and certainly to the authors. From this stems much of V et al.'s misunderstandings of his position. In steady motion of a classical fluid nobody thinks that momentum conservation is violated, or that momentum is shot upstream without cause because of the suction from the sinks. Similarly with momentum-energy in spacetime and the acceptance of an advanced causality. As for the CT invariance of the Feynman propagator, the causality asymmetry it entails is factlike, not lawlike. The geometrical counterpart of the symmetry between prediction and retrodiction and between retarded and advanced waves, as expressed in the alternative expressions
On the zigzagging causality EPR model: Answer to Vigier and coworkers and to Sutherland
Costa de Beauregard, O.
1987-08-01
The concept of “propagation in time” of Vigier and co-workers (V et al.) implies the idea of a supertime; it is thus alien to most Minkowskian pictures and certainly to mine. From this stems much of V et al.'s misunderstandings of my position. In steady motion of a classical fluid nobody thinks that “momentum conservation is violated,” or that “momentum is shot upstream without cause” because of the suction from the sinks! Similarly with momentum-energy in space-time and the acceptance of an advanced causality. As for the CT invariance of the Feynman propagator, the causality asymmetry it entails is factlike, not lawlike. The geometrical counterpart of the symmetry between prediction and retrodiction and between retarded and advanced waves, as expressed in the alternative expressions == for a transition amplitude between a preparation |A> and a measurement |B>, is CPT-invariant, not PT-invariant. These three expressions respectively illustrate the collapse, the retrocollapse, and the symmetric collapse-and-retrocollapse concepts. As for Sutherland's argument, what it “falsifies” is not my retrocausation concept but the hidden-variables assumption he has unwittingly made.
Hierarchies and causal relationships in interpretative models of the neoplastic process.
Bertolaso, Marta
2011-01-01
The aim of this paper is to present a critical analysis of the kind of biological systems identified in the main explanatory theories of cancer (i.e. Somatic Mutation Theory and Tissue Organization Field Theory) and how references to the hierarchical organization of these biological systems are used in their explanatory arguments. I will discuss these aspects in terms of the isolation of the "locus of control" (Bechtel and Richardson 2010); that is, the point at which decisions are made shaping the explanatory endeavour. In fact, the current view of the neoplastic process, not as a static circumstance but as an evolving molecular and cellular process, makes it evident that the choice of the right level of analysis is not self-evident. This focus clarifies some epistemological reasons for the divergence between reductionist and organicist accounts and seems to suggest that the basis for distinctions among causal relationships that scientists sometimes make can be found in the hierarchical character of complex biological systems. I will argue that these different causal relationships reflect different levels of epistemic concern.
Karabatsos, G.; Walker, S.G.
2010-01-01
Causal inference is central to educational research, where in data analysis the aim is to learn the causal effects of educational treatments on academic achievement, to evaluate educational policies and practice. Compared to a correlational analysis, a causal analysis enables policymakers to make more meaningful statements about the efficacy of…
Gates, Kathleen M; Molenaar, Peter C M; Hillary, Frank G; Ram, Nilam; Rovine, Michael J
2010-04-15
Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Directory of Open Access Journals (Sweden)
Hilary Zelko
Full Text Available BACKGROUND: Although scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications. METHODOLOGY/PRINCIPAL FINDINGS: Researchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers. CONCLUSIONS: The proposed
Representing Personal Determinants in Causal Structures.
Bandura, Albert
1984-01-01
Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…
Expectations and Interpretations during Causal Learning
Luhmann, Christian C.; Ahn, Woo-kyoung
2011-01-01
In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…
Diagnosability Analysis Considering Causal Interpretations for Differential Constraints
2012-01-01
This paper is focused on structural approaches to study diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system analytical redundancy relations. Simultaneous application of integral and diffe...
Kondratyuk, S
2000-01-01
Pion-loop corrections for Compton scattering are calculated in a novel approach based on the use of dispersion relations in a formalism obeying unitarity. The basic framework is presented, including an application to Compton scattering. In the approach the effects of the non-pole contribution arising from pion dressing are expressed in terms of (half-off-shell) form factors and the nucleon self-energy. These quantities are constructed through the application of dispersion integrals to the pole contribution of loop diagrams, the same as those included in the calculation of the amplitudes through a K-matrix formalism. The prescription of minimal substitution is used to restore gauge invariance. The resulting relativistic-covariant model combines constraints from unitarity, causality, and crossing symmetry.
Spectral Geometry and Causality
Kopf, T
1996-01-01
For a physical interpretation of a theory of quantum gravity, it is necessary to recover classical spacetime, at least approximately. However, quantum gravity may eventually provide classical spacetimes by giving spectral data similar to those appearing in noncommutative geometry, rather than by giving directly a spacetime manifold. It is shown that a globally hyperbolic Lorentzian manifold can be given by spectral data. A new phenomenon in the context of spectral geometry is observed: causal relationships. The employment of the causal relationships of spectral data is shown to lead to a highly efficient description of Lorentzian manifolds, indicating the possible usefulness of this approach. Connections to free quantum field theory are discussed for both motivation and physical interpretation. It is conjectured that the necessary spectral data can be generically obtained from an effective field theory having the fundamental structures of generalized quantum mechanics: a decoherence functional and a choice of...
Haase, D.
2009-04-01
Participation processes play a crucial role in implementing adaptive management in river basins. A range of different participative methods is being applied, however, little is known on their effectiveness in addressing the specific question or policy process at stake and their performance in different socio-economic and cultural settings. To shed light on the role of cultural settings on the outcomes of a participative process we carried out a comparative study of participation processes using group model building (GMB) in a European, a Central Asian, and an African river basin. We use an analytical framework which covers the goals, the role of science and stakeholders, the initiation and methods of the processes framed by very different cultural, socio-economic and biophysical conditions. Across all three basins, the GMB processes produced a shared understanding among all participants of the major water management issues in the respective river basin and common approaches to address them. The "ownership of the ideas" by the stakeholders, i.e. the topic to be addressed in a GMB process, is important for their willingness to contribute to such a participatory process. Differences, however, exist in so far that cultural and contextual constraints of the basin drive the way the GMB processes have been designed and how their results contribute to policy development.
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Bram eTucker
2015-10-01
Full Text Available A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of coexistence thinking, the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one's range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence, whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking, or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking. Results from three field studies suggest that (a informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross
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...
When two become one: the limits of causality analysis of brain dynamics.
Chicharro, Daniel; Ledberg, Anders
2012-01-01
Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.
When two become one: the limits of causality analysis of brain dynamics.
Directory of Open Access Journals (Sweden)
Daniel Chicharro
Full Text Available Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM. Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.
Inferring causal molecular networks: empirical assessment through a community-based effort.
Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach
2016-04-01
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
Kimura, Daisuke; Nakatani, Ken; Takeda, Tokunori; Fujita, Takashi; Sunahara, Nobuyuki; Inoue, Katsumi; Notoya, Masako
2015-01-01
The purpose of this study is to identify a potentiality factor that is a preventive factor for decline in cognitive function. Additionally, this study pursues to clarify the causal relationship between the each potential factor and its influence on cognitive function. Subjects were 366 elderly community residents (mean age 73.7 ± 6.4, male 51, female 315) who participated in the Taketoyo Project from 2007 to 2011. Factor analysis was conducted to identify groupings within mental, social, life, physical and cognitive functions. In order to detect clusters of 14 variables, the item scores were subjected to confirmatory factor analysis. We performed Structural Equation Modeling analysis to calculate the standardization coefficient and correlation coefficient for every factor. The cause and effect hypothesis model was used to gather two intervention theory hypotheses for dementia prevention (direct effect, indirect effect) in one system. Finally, we performed another Structural Equation Modeling analysis to calculate the standardization of the cause and effect hypothesis model. Social participation was found to be activated by the improvement of four factors, and in turn, activated "Social participation" acted on cognitive function.
A model comparison approach shows stronger support for economic models of fertility decline.
Shenk, Mary K; Towner, Mary C; Kress, Howard C; Alam, Nurul
2013-05-14
The demographic transition is an ongoing global phenomenon in which high fertility and mortality rates are replaced by low fertility and mortality. Despite intense interest in the causes of the transition, especially with respect to decreasing fertility rates, the underlying mechanisms motivating it are still subject to much debate. The literature is crowded with competing theories, including causal models that emphasize (i) mortality and extrinsic risk, (ii) the economic costs and benefits of investing in self and children, and (iii) the cultural transmission of low-fertility social norms. Distinguishing between models, however, requires more comprehensive, better-controlled studies than have been published to date. We use detailed demographic data from recent fieldwork to determine which models produce the most robust explanation of the rapid, recent demographic transition in rural Bangladesh. To rigorously compare models, we use an evidence-based statistical approach using model selection techniques derived from likelihood theory. This approach allows us to quantify the relative evidence the data give to alternative models, even when model predictions are not mutually exclusive. Results indicate that fertility, measured as either total fertility or surviving children, is best explained by models emphasizing economic factors and related motivations for parental investment. Our results also suggest important synergies between models, implicating multiple causal pathways in the rapidity and degree of recent demographic transitions.
On the detection of effective marketing instruments and causality in VAR models
Horváth, C.; Otter, P.W.
2000-01-01
Dynamic multivariate models become more and more popular in analyzing the behavior of competive marketing environments. Takada and Bass (1998), Dekimpe, Hanssens and Silva-Rosso (1999), and Dekimpe and Hanssens (1999) recommend to use Vector Autoregressive (VAR) models because they provide full-scal
Directory of Open Access Journals (Sweden)
Akihiro T Sasaki
2012-08-01
Full Text Available Automatic mimicry is based on the tight linkage between motor and perception action representations in which internal models play a key role. Based on the anatomical connection, we hypothesized that the direct effective connectivity from the posterior superior temporal sulcus (pSTS to the ventral premotor area (PMv formed an inverse internal model, converting visual representation into a motor plan, and that reverse connectivity formed a forward internal model, converting the motor plan into a sensory outcome of action. To test this hypothesis, we employed dynamic causal-modeling analysis with functional magnetic-resonance imaging. Twenty-four normal participants underwent a change-detection task involving two visually-presented balls that were either manually rotated by the investigator’s right hand (‘Hand’ or automatically rotated. The effective connectivity from the pSTS to the PMv was enhanced by hand observation and suppressed by execution, corresponding to the inverse model. Opposite effects were observed from the PMv to the pSTS, suggesting the forward model. Additionally, both execution and hand observation commonly enhanced the effective connectivity from the pSTS to the inferior parietal lobule (IPL, the IPL to the primary sensorimotor cortex (S/M1, the PMv to the IPL, and the PMv to the S/M1. Representation of the hand action therefore was implemented in the motor system including the S/M1. During hand observation, effective connectivity toward the pSTS was suppressed whereas that toward the PMv and S/M1 was enhanced. Thus the action-representation network acted as a dynamic feedback-control system during action observation.
Extraction of Textual Causal Relationships based on Natural Language Processing
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Sepideh Jamshidi-Nejad
2015-11-01
Full Text Available Natural language processing is a highly important subcategory in the wide area of artificial intelligence. Employing appropriate computational algorithms on sophisticated linguistic operations is the aim of natural language processing to extract and create computational theories from languages. In order to achieve this goal, the knowledge of linguists is needed in addition to computer science. In the field of linguistics, the syntactic and semantic relation of words and phrases and the extraction of causation is very significant which the latter is an information retrieval challenge. Recently, there is an increased attention towards the automatic extraction of causation from textual data sets. Although, previous research extracted the casual relations from uninterrupted data sets by using knowledge-based inference technologies and manual coding. Recently, finding comprehensive approaches for detection and extractions of causal arguments is a research area in the field of natural language processing.In this paper, a three-stepped approach is established through which, the position of words with syntax trees is obtained by extracting causation from causal and non-causal sentences of Web text. The arguments of events were extracted according to the dependency tree of phrases implemented by Python packages. Then potential causal relations were extracted by the extraction of specific nodes of the tree. In the final step, a statistical model is introduced for measuring the potential causal relations. Experimental results and evaluations with Recall, Precision and F-measure metrics show the accuracy and efficiency of the suggested model.
New Product Development and Innovation in the Maquiladora Industry: A Causal Model
Jorge Luis García-Alcaraz; Aidé Aracely Maldonado-Macías; Sandra Ivette Hernández-Hernández; Juan Luis Hernández-Arellano; Julio Blanco-Fernández; Juan Carlos Sáenz Díez-Muro
2016-01-01
Companies seek to stand out from their competitors and react to other competitive threats. Making a difference means doing things differently in order to create a product that other companies cannot provide. This can be achieved through an innovation process. This article analyses, by means of a structural equation model, the current situation of Mexican maquiladora companies, which face the constant challenge of product innovation. The model associates three success factors for new product d...
Bemana, Foruzan; Bemana, Simin; Farhadi, Payam; Shokrpour, Nasrin
2014-01-01
Nowadays burnout is a common issue in all health systems and therapeutic professions. Burnout is caused by job stressors and results in reduction in output, increase in absenteeism and health expenses, behavioral changes, and sometimes drugs abuse. Nonetheless, people who have hardy personalities experience less exhaustion. The present research aimed to present a causal model of antecedents with burnout to emphasize the intermediate role of hardy personality in the nurses working in the public hospitals of Shiraz, Iran. The study data were collected using the Nursing Burnout Scale questionnaire (Int J Nurs Stud. 2008;45(3):418-427). In addition, the structural equation method was used as a model in order to determine the relationship between the variables. The suggested pattern in this research was checked by Leasrel software, version 8.5. The study results showed that antecedents, such as incorrect supervision, responsibility, and workload, have a significant effect on burnout. However, mediated hardy personality had no effect on burnout. The results also showed that the people who had hardy personality could manage the stressful situations well and, consequently, rarely experience burnout. Overall, if the job stressors are existent in the job environment and the individuals cannot eradicate them, they will cause burnout outbreak.
Jones, Robert
2010-03-01
There are a wide range of views on causality. To some (e.g. Karl Popper) causality is superfluous. Bertrand Russell said ``In advanced science the word cause never occurs. Causality is a relic of a bygone age.'' At the other extreme Rafael Sorkin and L. Bombelli suggest that space and time do not exist but are only an approximation to a reality that is simply a discrete ordered set, a ``causal set.'' For them causality IS reality. Others, like Judea Pearl and Nancy Cartwright are seaking to build a complex fundamental theory of causality (Causality, Cambridge Univ. Press, 2000) Or perhaps a theory of causality is simply the theory of functions. This is more or less my take on causality.
Frisch, Mathias
2014-01-01
Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science.
Algorithms of causal inference for the analysis of effective connectivity among brain regions.
Chicharro, Daniel; Panzeri, Stefano
2014-01-01
In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl's causality, algorithms of inductive causation (IC and IC(*)) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity.
Algorithms of causal inference for the analysis of effective connectivity among brain regions
Directory of Open Access Journals (Sweden)
Daniel eChicharro
2014-07-01
Full Text Available In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC* provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM to analyze causal influences (effective connectivity among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g. measurement noise, hemodynamic responses, and time aggregation can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity.
Adams, Rick A; Bauer, Markus; Pinotsis, Dimitris; Friston, Karl J
2016-05-15
This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision - inferred by our behavioural DCM - correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.
New Product Development and Innovation in the Maquiladora Industry: A Causal Model
Directory of Open Access Journals (Sweden)
Jorge Luis García-Alcaraz
2016-07-01
Full Text Available Companies seek to stand out from their competitors and react to other competitive threats. Making a difference means doing things differently in order to create a product that other companies cannot provide. This can be achieved through an innovation process. This article analyses, by means of a structural equation model, the current situation of Mexican maquiladora companies, which face the constant challenge of product innovation. The model associates three success factors for new product development (product, organization, and production process characteristics as independent latent variables with benefits gained by customers and companies (dependent latent variables. Results show that, in the Mexican maquiladora sector, organizational characteristics and production processes characteristics explain only 31% of the variability (R2 = 0.31, and it seems necessary to integrate other aspects. The relationship between customer benefits and company benefits explains 58% of the variability, the largest proportion in the model (R2 = 0.58.
Identifying Causal Effects with Computer Algebra
García-Puente, Luis David; Sullivant, Seth
2010-01-01
The long-standing identification problem for causal effects in graphical models has many partial results but lacks a systematic study. We show how computer algebra can be used to either prove that a causal effect can be identified, generically identified, or show that the effect is not generically identifiable. We report on the results of our computations for linear structural equation models, where we determine precisely which causal effects are generically identifiable for all graphs on three and four vertices.
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.
Multiple Model Approaches to Modelling and Control,
DEFF Research Database (Denmark)
on the ease with which prior knowledge can be incorporated. It is interesting to note that researchers in Control Theory, Neural Networks,Statistics, Artificial Intelligence and Fuzzy Logic have more or less independently developed very similar modelling methods, calling them Local ModelNetworks, Operating...... of introduction of existing knowledge, as well as the ease of model interpretation. This book attempts to outlinemuch of the common ground between the various approaches, encouraging the transfer of ideas.Recent progress in algorithms and analysis is presented, with constructive algorithms for automated model...
Possel, Patrick; Seemann, Simone; Ahrens, Stefanie; Hautzinger, Martin
2006-01-01
In Dodge's model of "social information processing" depression is the result of a linear sequence of five stages of information processing ("Annu Rev Psychol" 44: 559-584, 1993). These stages follow a person's reaction to situational stimuli, such that each stage of information processing mediates the relationship between earlier and later stages.…
Explaining prosocial intentions : Testing causal relationships in the norm activation model
Steg, Linda; de Groot, Judith
2010-01-01
This paper examines factors influencing prosocial intentions. On the basis of the norm activation model (NAM), we propose that four variables influence prosocial intentions or behaviours: ( I) personal norms (PN), reflecting feelings of moral obligation to engage in prosocial behaviour, (2) awarenes
Causal Client Models in Selecting Effective Interventions: A Cognitive Mapping Study
Kwaadsteniet, L. de; Hagmayer, Y.; Krol, N.P.C.M.; Witteman, C.L.M.
2010-01-01
An important reason to choose an intervention to treat psychological problems of clients is the expectation that the intervention will be effective in alleviating the problems. The authors investigated whether clinicians base their ratings of the effectiveness of interventions on models that they co
Normalizability analysis of the generalized quantum electrodynamics from the causal point of view
Bufalo, R; Soto, D E
2015-01-01
The causal perturbation theory is an axiomatic perturbative theory of the S-matrix. This formalism has as its essence the following axioms: causality, Lorentz invariance and asymptotic conditions. Any other property must be showed via the inductive method order-by-order and, of course, it depends on the particular physical model. In this work we shall study the normalizability of the generalized quantum electrodynamics in the framework of the causal approach. Furthermore, we analyse the implication of the gauge invariance onto the model and obtain the respective Ward-Takahashi-Fradkin identities.
Model Construct Based Enterprise Model Architecture and Its Modeling Approach
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to support enterprise integration, a kind of model construct based enterprise model architecture and its modeling approach are studied in this paper. First, the structural makeup and internal relationships of enterprise model architecture are discussed. Then, the concept of reusable model construct (MC) which belongs to the control view and can help to derive other views is proposed. The modeling approach based on model construct consists of three steps, reference model architecture synthesis, enterprise model customization, system design and implementation. According to MC based modeling approach a case study with the background of one-kind-product machinery manufacturing enterprises is illustrated. It is shown that proposal model construct based enterprise model architecture and modeling approach are practical and efficient.
Marsh, Herbert W; Trautwein, Ulrich; Lüdtke, Oliver; Köller, Olaf; Baumert, Jürgen
2005-01-01
Reciprocal effects models of longitudinal data show that academic self-concept is both a cause and an effect of achievement. In this study this model was extended to juxtapose self-concept with academic interest. Based on longitudinal data from 2 nationally representative samples of German 7th-grade students (Study 1: N = 5,649, M age = 13.4; Study 2: N = 2,264, M age = 13.7 years), prior self-concept significantly affected subsequent math interest, school grades, and standardized test scores, whereas prior math interest had only a small effect on subsequent math self-concept. Despite stereotypic gender differences in means, linkages relating these constructs were invariant over gender. These results demonstrate the positive effects of academic self-concept on a variety of academic outcomes and integrate self-concept with the developmental motivation literature.
Novel Causality in Consumer’s Online Behavior: Ecommerce Success Model
Directory of Open Access Journals (Sweden)
Amna Khatoon
2016-12-01
Full Text Available Online shopping (e-Shopping has grown at a rapid pace with the advancement in modern web technologies, there are then socio and technical aspects (factors in the mentioned e-shopping. The following research paper highlights some mandatory socio-technical factors affecting consumer’s behavior in online shopping environment. In this work a comprehensive conceptual model is put forward based on proposed reform DeLone and McLean Success Model for Information Systems. This model is used for the assessment of the success of eCommerce web portals. Approximately thirteen different hypotheses are proposed on the bases of this methodology which represent the cause and effect relationship among the various variables affecting consumer’s online buying behavior. Further this work is simulated in iThink technology to show prominently that consumer’s satisfaction and trust directly affects productivity of the organization. For development organizations the proposed methodology is valuable because it will facilitate in building the eCommerce websites, web portals whereas retailers can improve the productivity of their organization by accomplishing this.
Directory of Open Access Journals (Sweden)
Idil Kokal
Full Text Available Studies investigating joint actions have suggested a central role for the putative mirror neuron system (pMNS because of the close link between perception and action provided by these brain regions [1], [2], [3]. In contrast, our previous functional magnetic resonance imaging (fMRI experiment demonstrated that the BOLD response of the pMNS does not suggest that it directly integrates observed and executed actions during joint actions [4]. To test whether the pMNS might contribute indirectly to the integration process by sending information to brain areas responsible for this integration (integration network, here we used Granger causality mapping (GCM [5]. We explored the directional information flow between the anterior sites of the pMNS and previously identified integrative brain regions. We found that the left BA44 sent more information than it received to both the integration network (left thalamus, right middle occipital gyrus and cerebellum and more posterior nodes of the pMNS (BA2. Thus, during joint actions, two anatomically separate networks therefore seem effectively connected and the information flow is predominantly from anterior to posterior areas of the brain. These findings suggest that the pMNS is involved indirectly in joint actions by transforming observed and executed actions into a common code and is part of a generative model that could predict the future somatosensory and visual consequences of observed and executed actions in order to overcome otherwise inevitable neural delays.
Bollen, Kenneth A; Lennox, Richard D; Dahly, Darren L
2009-05-01
Researchers are often faced with the task of trying to measure abstract concepts. The most common approach is to use multiple indicators that reflect an underlying latent variable. However, this 'effect indicator' measurement model is not always appropriate; sometimes the indicators instead cause the construct of interest. While the notion of 'causal indicators' has been known for some time, it is still too often ignored. However, there are limited means to determine whether a possible indicator should be treated as a cause or an effect of the latent construct of interest. Perhaps the best empirical way is to use the vanishing tetrad test (VTT), yet this method is still often overlooked. We speculate that one reason for this is the lack of published examples of its use in practice, written for an audience without extensive statistical training. The goal of this paper was to help fill this gap in the literature-to provide a basic example of how to use the VTT. We illustrated the VTT by looking at multiple items from a health related quality of life instrument that seem more likely to cause the latent variable rather than the other way around.
The continuum limit of causal fermion systems from Planck scale structures to macroscopic physics
Finster, Felix
2016-01-01
This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries". The dynamics is described by a novel variational principle, called the causal action principle. In addition to the basics, the book provides all the necessary mathematical background and explains how the causal action principle gives rise to the interactions of the standard model plus gravity on the level of second-quantized fermionic fields coupled to classical bosonic fields. The focus is on getting a mathematically sound connection between causal fermion systems and physical systems in Minkowski space. The book is intended for graduate students e...
Independence and dependence in human causal reasoning.
Rehder, Bob
2014-07-01
Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult's causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned "associatively," that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects' assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.
How prescriptive norms influence causal inferences.
Samland, Jana; Waldmann, Michael R
2016-11-01
Recent experimental findings suggest that prescriptive norms influence causal inferences. The cognitive mechanism underlying this finding is still under debate. We compare three competing theories: The culpable control model of blame argues that reasoners tend to exaggerate the causal influence of norm-violating agents, which should lead to relatively higher causal strength estimates for these agents. By contrast, the counterfactual reasoning account of causal selection assumes that norms do not alter the representation of the causal model, but rather later causal selection stages. According to this view, reasoners tend to preferentially consider counterfactual states of abnormal rather than normal factors, which leads to the choice of the abnormal factor in a causal selection task. A third view, the accountability hypothesis, claims that the effects of prescriptive norms are generated by the ambiguity of the causal test question. Asking whether an agent is a cause can be understood as a request to assess her causal contribution but also her moral accountability. According to this theory norm effects on causal selection are mediated by accountability judgments that are not only sensitive to the abnormality of behavior but also to mitigating factors, such as intentionality and knowledge of norms. Five experiments are presented that favor the accountability account over the two alternative theories.
基于图模型方法的Granger因果性检验∗%Granger Causality Detecting Based on Graphical Modelling
Institute of Scientific and Technical Information of China (English)
魏岳嵩
2016-01-01
The Granger causality is an important criterion for measuring the dynamic relation-ship among system variables. In this paper, we apply the graphical model method to explore the Granger causal relations among variables. The Granger causality graph is established and its structural identification is investigated based on the conditional mutual information and permutation test. The test statistics is estimated using the correlation integral of chaos theory and its limiting distribution is proved. Finally, the Granger causality among main international stock markets is investigated using the proposed method.%Granger因果性是衡量系统变量间动态关系的重要依据。本文利用图模型方法研究变量间的Granger因果性，建立了Granger因果图。基于条件互信息和置换检验法建立了Granger因果图结构的辨识方法，利用混沌理论中的关联积分估计相应的检验统计量，给出了统计量的渐进分布，并用所给方法研究国际主要股市间的Granger因果关系。
An Introduction to Causal Inference
2009-11-02
legitimize causal inference, has removed causation from its natural habitat, and distorted its face beyond recognition. This exclusivist attitude is...In contrast, when the mediation problem is approached from an exclusivist potential-outcome viewpoint, void of the structural guidance of Eq. (28
A causal model of coping and well-being in elderly people with arthritis.
Downe-Wamboldt, B L; Melanson, P M
1998-06-01
The purpose of this longitudinal study was to test a model of the relationships among social economic status, gender, severity of impairment, stress emotions, coping strategies and psychological well-being. A sample of 78 elderly women and men, 60 years old or over, and diagnosed as having rheumatoid arthritis since mid-life, volunteered to participate in the study. Twelve months later, 64 of these elderly people were re-interviewed. Path analysis was used to examine the empirical import of the Lazarus and Folkman theory of stress and coping. Analysis of variance for repeated measures was used to test for changes over time among the study variable. A consistent relationship between severity of impairment, emotions, coping strategies and psychological well-being emerged from the data at time one and time two. Choice of coping strategies and psychological well-being were primarily influenced by emotions. The best predictor of psychological well-being at both time periods was the stress emotion of challenge. At both time periods, optimistic and self-reliant coping strategies were used most often and evasive and emotive strategies the least.
Janardhan, Sujatha
2012-01-01
We present a short review of geometric and algebraic approach to causal cones and describe cone preserving transformations and their relationship with causal structure related to special and general theory of relativity. We describe Lie groups, especially matrix Lie groups, homogeneous and symmetric spaces and causal cones and certain implications of these concepts in special and general theory of relativity related to causal structure and topology of space-time. We compare and contrast the results on causal relations with those in the literature for general space-times and compare these relations with K-causal maps. We also describe causal orientations and their implications for space-time topology and discuss some more topologies on space-time which arise as an application of domain theory.
Hydraulic Modeling of Lock Approaches
2016-08-01
cation was that the guidewall design changed from a solid wall to one on pilings in which water was allowed to flow through and/or under the wall ...develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the...magnitudes and directions at lock approaches for open river conditions. The meshes were developed using the Surface- water Modeling System. The two
Oldmeadow, Christopher; Hure, Alexis; Luu, Judy; Loxton, Deborah
2017-01-01
Background Type 2 diabetes is associated with significant morbidity and mortality. Modifiable risk factors have been found to contribute up to 60% of type 2 diabetes risk. However, type 2 diabetes continues to rise despite implementation of interventions based on traditional risk factors. There is a clear need to identify additional risk factors for chronic disease prevention. The aim of this study was to examine the relationship between perceived stress and type 2 diabetes onset, and partition the estimates into direct and indirect effects. Methods and findings Women born in 1946–1951 (n = 12,844) completed surveys for the Australian Longitudinal Study on Women’s Health in 1998, 2001, 2004, 2007 and 2010. The total causal effect was estimated using logistic regression and marginal structural modelling. Controlled direct effects were estimated through conditioning in the regression model. A graded association was found between perceived stress and all mediators in the multivariate time lag analyses. A significant association was found between hypertension, as well as physical activity and body mass index, and diabetes, but not smoking or diet quality. Moderate/high stress levels were associated with a 2.3-fold increase in the odds of diabetes three years later, for the total estimated effect. Results were only slightly attenuated when the direct and indirect effects of perceived stress on diabetes were partitioned, with the mediators only explaining 10–20% of the excess variation in diabetes. Conclusions Perceived stress is a strong risk factor for type 2 diabetes. The majority of the effect estimate of stress on diabetes risk is not mediated by the traditional risk factors of hypertension, physical activity, smoking, diet quality, and body mass index. This gives a new pathway for diabetes prevention trials and clinical practice. PMID:28222165
Breaking the arrows of causality
DEFF Research Database (Denmark)
Valsiner, Jaan
2014-01-01
Theoretical models of catalysis have proven to bring with them major breakthroughs in chemistry and biology, from the 1830s onward. It can be argued that the scientific status of chemistry has become established through the move from causal to catalytic models. Likewise, the central explanatory...
Causal Indicators Can Help to Interpret Factors
Bentler, Peter M.
2016-01-01
The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-10-05
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches.
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-10-01
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches.
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-01-01
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches. PMID:26434769
Causality for nonlocal phenomena
Eckstein, Michał
2015-01-01
Drawing from the theory of optimal transport we propose a rigorous notion of a causal relation for Borel probability measures on a given spacetime. To prepare the ground, we explore the borderland between causality, topology and measure theory. We provide various characterisations of the proposed causal relation, which turn out to be equivalent if the underlying spacetime has a sufficiently robust causal structure. We also present the notion of the 'Lorentz-Wasserstein distance' and study its basic properties. Finally, we discuss how various results on causality in quantum theory, aggregated around Hegerfeldt's theorem, fit into our framework.
Institute of Scientific and Technical Information of China (English)
李晓田; 王安麟
2016-01-01
Bond graph modeling for linear actuator revolute joint mechanism which uses Lagrange approach or stiff compliance approach with new elements inserted causes stiff equation problems, and causality in bond graph is not classified and modularized. A causality classification method is proposed for modeling with this kind of mechanism. Classifications for elements are classified as main-body,cylinder and lever type by analysis of element bond graphs and connection patterns, and joints are classified as root,pusher,pushed and load type to maintain the original causal link. Causality determination principles are proposed by these definitions to make modeling orderliness. An excavator mechanism modeling and simulation demonstration proved that proposed method is suitable for mechanism cases of earthmoving vehicles.%采用键合图方法对直线驱动铰链机构进行动力学建模时，传统的虚拟弹性铰接点或拉格朗日乘子法会引入新的动态环节导致出现刚性问题，势流关系混乱难以规范化和模块化。提出了一种针对该机构的键合图因果关系分类整理方法，通过元件接口势流因果分析及元件间连接规则整理两个方面，分类为液压缸、主部件和连杆三种元件，并分别统一各铰接点势流因果关系接口为根点、外力顶点、力输出点和负载点，使得刚体元件接口的因果关系得以延续。在此基础上建立了元件间连接的规范方法以简化建模过程。最后反铲挖掘机的建模及仿真示例证明了本方法对于工程机械的常见工作机构均可适用。
Approaches to Modeling of Recrystallization
Directory of Open Access Journals (Sweden)
Håkan Hallberg
2011-10-01
Full Text Available Control of the material microstructure in terms of the grain size is a key component in tailoring material properties of metals and alloys and in creating functionally graded materials. To exert this control, reliable and efficient modeling and simulation of the recrystallization process whereby the grain size evolves is vital. The present contribution is a review paper, summarizing the current status of various approaches to modeling grain refinement due to recrystallization. The underlying mechanisms of recrystallization are briefly recollected and different simulation methods are discussed. Analytical and empirical models, continuum mechanical models and discrete methods as well as phase field, vertex and level set models of recrystallization will be considered. Such numerical methods have been reviewed previously, but with the present focus on recrystallization modeling and with a rapidly increasing amount of related publications, an updated review is called for. Advantages and disadvantages of the different methods are discussed in terms of applicability, underlying assumptions, physical relevance, implementation issues and computational efficiency.
Phenomenology of Causal Dynamical Triangulations
Mielczarek, Jakub
2015-01-01
The four dimensional Causal Dynamical Triangulations (CDT) approach to quantum gravity is already more than ten years old theory with numerous unprecedented predictions such as non-trivial phase structure of gravitational field and dimensional running. Here, we discuss possible empirical consequences of CDT derived based on the two features of the approach mentioned above. A possibility of using both astrophysical and cosmological observations to test CDT is discussed. We show that scenarios which can be ruled out at the empirical level exist.
Saladié, Òscar; Santos-Lacueva, Raquel
2016-02-01
One of the main objectives of municipal waste management policies is to improve separate collection, both quantitatively and qualitatively. Several factors influence people behavior to recycling and, consequently, they play an important role to achieve the goals proposed in the management policies. People can improve separate collection rates because of a wide range of causes with different weight. Here, we have determined the uplift in probability to improve separate collection of municipal waste created by the awareness campaigns among 806 undergraduate students at Universitat Rovira i Virgili (Catalonia) by means of the Causal Chain Approach, a probabilistic method. A 73.2% state having improved separate collection in recent years and the most of them (75.4%) remember some awareness campaign. The results show the uplift in probability to improve separate collection attributable to the awareness campaigns is 17.9%. They should be taken into account by policy makers in charge of municipal waste management. Nevertheless, it must be assumed an awareness campaign will never be sufficient to achieve the objectives defined in municipal waste management programmes.
The transfer matrix in four-dimensional Causal Dynamical Triangulations
Görlich, Andrzej
2013-01-01
Causal Dynamical Triangulations is a background independent approach to quantum gravity. In this paper we introduce a phenomenological transfer matrix model, where at each time step a reduced set of quantum states is used. The states are solely characterized by the discretized spatial volume. Using Monte Carlo simulations we determine the effective transfer matrix elements and extract the effective action for the scale factor. In this framework no degrees of freedom are frozen, however, the obtained action agrees with the minisuperspace model.
Institute of Scientific and Technical Information of China (English)
魏岳嵩; 杜翠真
2014-01-01
确定变量间的因果关系是时间序列分析的重要内容。传统的图模型因果推断算法有着明显的局限性，要求模型是线性的且噪声项服从Gauss分布。本文利用图模型方法辨识非线性结构向量自回归模型变量间的因果关系，给出了一种基于互信息和条件互信息的非线性结构向量自回归因果图模型结构的非参数辨识方法。数值模拟结果验证了方法的有效性。%It is important to detect and clarify the cause-effect relationships among variables in time series analysis. Traditional graphical models causality inference methods have a salient limitation that the model must be linear and with Gaussian noise. In this paper, we apply the graphical models to infer the causal relationships a-mong variables of nonlinear structural vector autoregressive models. We propose a nonparametric method which employs both the mutual information and condi-tional mutual information to identify the causal structure of nonlinear structural vector autoregressive causal graph model. Numerical simulations demonstrate the effectiveness of the method.
Institute of Scientific and Technical Information of China (English)
干红华; 潘云鹤
2001-01-01
Causal reasoning is the most important feature in law consultant systems. This paper analy-ses the structure of law clauses,proposes a representation model for law knowledge in terms of causal relationships and nonmonotonic reasoning models based on it. These models are successfully applied in the implementation of NBU-CALA+ ,a law expert consultant system for case analysis and interpreta-tion.
Information thermodynamics on causal networks.
Ito, Sosuke; Sagawa, Takahiro
2013-11-01
We study nonequilibrium thermodynamics of complex information flows induced by interactions between multiple fluctuating systems. Characterizing nonequilibrium dynamics by causal networks (i.e., Bayesian networks), we obtain novel generalizations of the second law of thermodynamics and the fluctuation theorem, which include an informational quantity characterized by the topology of the causal network. Our result implies that the entropy production in a single system in the presence of multiple other systems is bounded by the information flow between these systems. We demonstrate our general result by a simple model of biochemical adaptation.
Introductive remarks on causal inference
Directory of Open Access Journals (Sweden)
Silvana A. Romio
2013-05-01
Full Text Available One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.
Ghasemi, Mahdieh; Mahloojifar, Ali
2013-01-01
Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients w...
Dynamics of safety performance and culture: a group model building approach.
Goh, Yang Miang; Love, Peter E D; Stagbouer, Greg; Annesley, Chris
2012-09-01
The management of occupational health and safety (OHS) including safety culture interventions is comprised of complex problems that are often hard to scope and define. Due to the dynamic nature and complexity of OHS management, the concept of system dynamics (SD) is used to analyze accident prevention. In this paper, a system dynamics group model building (GMB) approach is used to create a causal loop diagram of the underlying factors influencing the OHS performance of a major drilling and mining contractor in Australia. While the organization has invested considerable resources into OHS their disabling injury frequency rate (DIFR) has not been decreasing. With this in mind, rich individualistic knowledge about the dynamics influencing the DIFR was acquired from experienced employees with operations, health and safety and training background using a GMB workshop. Findings derived from the workshop were used to develop a series of causal loop diagrams that includes a wide range of dynamics that can assist in better understanding the causal influences OHS performance. The causal loop diagram provides a tool for organizations to hypothesize the dynamics influencing effectiveness of OHS management, particularly the impact on DIFR. In addition the paper demonstrates that the SD GMB approach has significant potential in understanding and improving OHS management.
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...
2015-01-01
Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases. Causal relationships can also be found using some well designed observational studies, but they require domain experts' knowledge and the process is normally time consuming. Hence there is a need for scalable and automated methods for c...
A Brief Introduction to Temporality and Causality
Karimi, Kamran
2010-01-01
Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.
Energy Technology Data Exchange (ETDEWEB)
Nazlioglu, Saban, E-mail: snazlioglu@pau.edu.tr [Department of Econometrics, Pamukkale University, Denizli (Turkey); Lebe, Fuat, E-mail: fuat.lebe@bozok.edu.tr [Department of Economics, Bozok University, Yozgat (Turkey); Kayhan, Selim, E-mail: selim.kayhan@bozok.edu.tr [Department of Economics, Bozok University, Yozgat (Turkey)
2011-10-15
The purpose of this study is to determine the direction causality between nuclear energy consumption and economic growth in OECD countries. The empirical model that includes capital and labor force as the control variables is estimated for the panel of fourteen OECD countries during the period 1980-2007. Apart from the previous studies in the nuclear energy consumption and economic growth relationship, this study utilizes the novel panel causality approach, which allows both cross-sectional dependency and heterogeneity across countries. The findings show that there is no causality between nuclear energy consumption and economic growth in eleven out of fourteen cases, supporting the neutrality hypothesis. As a sensitivity analysis, we also conduct Toda-Yamamoto time series causality method and find out that the results from the panel causality analysis are slightly different than those from the time-series causality analysis. Thereby, we can conclude that the choice of statistical tools in analyzing the nature of causality between nuclear energy consumption and economic growth may play a key role for policy implications. - Highlights: > Causality between nuclear energy consumption and economic growth is examined for OECD countries. > Panel causality method, which allows cross-sectional dependency and heterogeneity, is utilized. > The neutrality hypothesis is supported.
Causality problem in Economic Science
Directory of Open Access Journals (Sweden)
JOSÉ LUIS RETOLAZA
2007-12-01
Full Text Available The main point of the paper is the problem of the economy to be consider like a science in the most strict term of the concept. In the first step we are going to tackle a presentation about what we understand by science to subsequently present some of the fallacies which have bring certain scepticism about the scientific character of the investigation in economy, to know: 1 The differences between hard and weak sciences -physics and social; 2 The differences between paradigm, —positivist and phenomenological— 3 The differences between physic causalityand historic causality. In the second step we are going to talk about two fundamental problems which are questioned: 1 the confusion between ontology and gnoseology and, 2 the erroneous concept of causality that commonly is used. In the last step of the paper we are going over the recent models of «causal explanation» and we suggest the probabilistic casualty development next with a more elaborated models of causal explanation, like a way to conjugate the scientific severity with the possibility to tackle complex economic realities.
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…
Causality and Lifshitz Holography
Energy Technology Data Exchange (ETDEWEB)
Koroteev, Peter [Department of Physics and Astronomy, University of Minnesota, 116 Church Street S.E., Minneapolis, MN 55455 (United States)
2011-07-15
We study signal propagation in theories with Lifshitz scaling using the gravity dual and show that backgrounds with z<1 are incompatible with causality of the strongly coupled theory. We argue that causality violations in z<1 theories show up in boundary correlation functions as superluminal modes.
Causality in Europeanization Research
DEFF Research Database (Denmark)
Lynggaard, Kennet
2012-01-01
Discourse analysis as a methodology is perhaps not readily associated with substantive causality claims. At the same time the study of discourses is very much the study of conceptions of causal relations among a set, or sets, of agents. Within Europeanization research we have seen endeavours...... 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......, it suggests that discourse analysis and the study of causality are by no means opposites. The study of Europeanization discourses may even be seen as an essential step in the move towards claims of causality in Europeanization research. This chapter deals with the question of how we may move from the study...
Directory of Open Access Journals (Sweden)
Thomas eWidlok
2014-11-01
Full Text Available Cognitive Scientists interested in causal cognition increasingly search for evidence from non-WEIRD people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition.
A Structural Equation Approach to Models with Spatial Dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it poss
A structural equation approach to models with spatial dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it poss
Causality in physiological signals.
Müller, Andreas; Kraemer, Jan F; Penzel, Thomas; Bonnemeier, Hendrik; Kurths, Jürgen; Wessel, Niels
2016-05-01
Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The
Causal inference in obesity research.
Franks, P W; Atabaki-Pasdar, N
2017-03-01
Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.
Institute of Scientific and Technical Information of China (English)
魏岳嵩; 田铮
2011-01-01
传统的两变量Granger因果分析法容易产生伪因果关系,且不能刻画变量间的同期因果性.利用图模型方法研究多维时间序列变量间Granger因果关系,通过Granger因果图的建立将问题转化为Granger因果图结构的辨识问题,利用局部密度估计法构造相应的辨识统计量,采用bootstrap方法来确定检验统计量的原分布.模拟分析以及对于中国股市间Granger因果关系的研究说明了该方法的有效性.%Traditional two-variable Granger causality analysis method is prone to inducing spurious causal relationship and cannot portray the immediate causal relationship. This paper explores how to use graphical model methods to analyze the Granger causality graphs among components of multivariate time series. Granger causality graphs of time series is presented and the structural identification problem of Granger causality graph is investigated. A statistic based on local density estimator is proposed, and a bootstrap methods is considered for determining the null distribution of the test statistic. The validity of the proposed method is confirmed by simulations analysis and investigating the Granger causal relationships of the China's stock market.
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...
Sinha, Shriprakash
2016-01-01
Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational mo...
On the spectral formulation of Granger causality.
Chicharro, D
2011-12-01
Spectral measures of causality are used to explore the role of different rhythms in the causal connectivity between brain regions. We study several spectral measures related to Granger causality, comprising the bivariate and conditional Geweke measures, the directed transfer function, and the partial directed coherence. We derive the formulation of dependence and causality in the spectral domain from the more general formulation in the information-theory framework. We argue that the transfer entropy, the most general measure derived from the concept of Granger causality, lacks a spectral representation in terms of only the processes associated with the recorded signals. For all the spectral measures we show how they are related to mutual information rates when explicitly considering the parametric autoregressive representation of the processes. In this way we express the conditional Geweke spectral measure in terms of a multiple coherence involving innovation variables inherent to the autoregressive representation. We also link partial directed coherence with Sims' criterion of causality. Given our results, we discuss the causal interpretation of the spectral measures related to Granger causality and stress the necessity to explicitly consider their specific formulation based on modeling the signals as linear Gaussian stationary autoregressive processes.
Omission of Causal Indicators: Consequences and Implications for Measurement
Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.
2016-01-01
One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…
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…
Scalar Field Green Functions on Causal Sets
Ahmed, S. Nomaan; Dowker, Fay; Surya, Sumati
2017-01-01
We examine the validity and scope of Johnston's models for scalar field retarded Green functions on causal sets in 2 and 4 dimensions. As in the continuum, the massive Green function can be obtained from the massless one, and hence the key task in causal set theory is to first identify the massless Green function. We propose that the 2-d model provides a Green function for the massive scalar field on causal sets approximated by any topologically trivial 2 dimensional spacetime. We explicitly ...
Intrinsic Universality of Causal Graph Dynamics
Directory of Open Access Journals (Sweden)
Simon Martiel
2013-09-01
Full Text Available Causal graph dynamics are transformations over graphs that capture two important symmetries of physics, namely causality and homogeneity. They can be equivalently defined as continuous and translation invariant transformations or functions induced by a local rule applied simultaneously on every vertex of the graph. Intrinsic universality is the ability of an instance of a model to simulate every other instance of the model while preserving the structure of the computation at every step of the simulation. In this work we present the construction of a family of intrinsically universal instances of causal graphs dynamics, each instance being able to simulate a subset of instances.
Directory of Open Access Journals (Sweden)
Nikolaos Dritsakis
2016-03-01
Full Text Available This paper explores the relationship between trade openness and economic growth using data for the thirteen newest European Union members. The study covers the period of 1995–2013. We have applied panel cointegration and causality approaches to examine the long-run and the causal relationship between the variables. Empirical results confirm the presence of a cointegrating vector between trade openness and economic growth, in this group of the thirteen countries. An error correction model (ECM, followed by the two steps of Engle and Granger was used to capture the short and long-run dynamics. The impact of economic growth and trade openness is found to be positive. Finally, the panel Granger causality analysis reveals a unidirectional causal relationship running from trade openness to economic growth, both in the short and in the long-run
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.
Runnqvist, Elin; Bonnard, Mireille; Gauvin, Hanna S; Attarian, Shahram; Trébuchon, Agnès; Hartsuiker, Robert J; Alario, F-Xavier
2016-08-01
Some language processing theories propose that, just as for other somatic actions, self-monitoring of language production is achieved through internal modeling. The cerebellum is the proposed center of such internal modeling in motor control, and the right cerebellum has been linked to an increasing number of language functions, including predictive processing during comprehension. Relating these findings, we tested whether the right posterior cerebellum has a causal role for self-monitoring of speech errors. Participants received 1 Hz repetitive transcranial magnetic stimulation during 15 min to lobules Crus I and II in the right hemisphere, and, in counterbalanced orders, to the contralateral area in the left cerebellar hemisphere (control) in order to induce a temporary inactivation of one of these zones. Immediately afterwards, they engaged in a speech production task priming the production of speech errors. Language production was impaired after right compared to left hemisphere stimulation, a finding that provides evidence for a causal role of the cerebellum during language production. We interpreted this role in terms of internal modeling of upcoming speech through a verbal working memory process used to prevent errors.
Validation of Modeling Flow Approaching Navigation Locks
2013-08-01
instrumentation, direction vernier . ........................................................................ 8 Figure 11. Plan A lock approach, upstream approach...13-9 8 Figure 9. Tools and instrumentation, bracket attached to rail. Figure 10. Tools and instrumentation, direction vernier . Numerical model
Energy Technology Data Exchange (ETDEWEB)
Tipler, F.J.
1977-08-01
Causally symmetric spacetimes are spacetimes with J/sup +/(S) isometric to J/sup -/(S) for some set S. We discuss certain properties of these spacetimes, showing for example that, if S is a maximal Cauchy surface with matter everywhere on S, then the spacetime has singularities in both J/sup +/(S) and J/sup -/(S). We also consider totally vicious spacetimes, a class of causally symmetric spacetimes for which I/sup +/(p) =I/sup -/(p) = M for any point p in M. Two different notions of stability in general relativity are discussed, using various types of causally symmetric spacetimes as starting points for perturbations.
Locally Causal Dynamical Triangulations in Two Dimensions
Loll, Renate
2015-01-01
We analyze the universal properties of a new two-dimensional quantum gravity model defined in terms of Locally Causal Dynamical Triangulations (LCDT). Measuring the Hausdorff and spectral dimensions of the dynamical geometrical ensemble, we find numerical evidence that the continuum limit of the model lies in a new universality class of two-dimensional quantum gravity theories, inequivalent to both Euclidean and Causal Dynamical Triangulations.
Model Mapping Approach Based on Ontology Semantics
Directory of Open Access Journals (Sweden)
Jinkui Hou
2013-09-01
Full Text Available The mapping relations between different models are the foundation for model transformation in model-driven software development. On the basis of ontology semantics, model mappings between different levels are classified by using structural semantics of modeling languages. The general definition process for mapping relations is explored, and the principles of structure mapping are proposed subsequently. The approach is further illustrated by the mapping relations from class model of object oriented modeling language to the C programming codes. The application research shows that the approach provides a theoretical guidance for the realization of model mapping, and thus can make an effective support to model-driven software development
Zinoviev, Yury M
2012-01-01
The equations of the relativistic causal Newton gravity law for the planets of the solar system are studied in the approximation when the Sun rests at the coordinates origin and the planets do not iteract between each other.
Confounding Equivalence in Causal Inference
Pearl, Judea
2012-01-01
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same bias-reducing potential under adjustment. The test re- quires that one of the following two condi- tions holds: either (1) both sets are admis- sible (i.e., satisfy the back-door criterion) or (2) the Markov boundaries surrounding the manipulated variable(s) are identical in both sets. Applications to covariate selection and model testing are discussed.
In silico model-based inference: a contemporary approach for hypothesis testing in network biology.
Klinke, David J
2014-01-01
Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics.
Arrighi, Pablo
2016-01-01
Consider a graph having quantum systems lying at each node. Suppose that the whole thing evolves in discrete time steps, according to a global, unitary causal operator. By causal we mean that information can only propagate at a bounded speed, with respect to the distance given by the graph. Suppose, moreover, that the graph itself is subject to the evolution, and may be driven to be in a quantum superposition of graphs---in accordance to the superposition principle. We show that these unitary causal operators must decompose as a finite-depth circuit of local unitary gates. This unifies a result on Quantum Cellular Automata with another on Reversible Causal Graph Dynamics. Along the way we formalize a notion of causality which is valid in the context of quantum superpositions of time-varying graphs, and has a number of good properties. Keywords: Quantum Lattice Gas Automata, Block-representation, Curtis-Hedlund-Lyndon, No-signalling, Localizability, Quantum Gravity, Quantum Graphity, Causal Dynamical Triangula...
Institute of Scientific and Technical Information of China (English)
魏岳嵩; 田铮; 陈占寿
2011-01-01
Grangerl因果性是衡量系统变量间动态关系的重要依据．传统的两变量Grangerl因果分析法容易产生伪因果关系，且不能刻画变量间的即时因果性．本文利用图模型方法研究时间序列变量间的Grangerl因果关系，建立了时间序列Granger因果图，提出Grangerl因果图的条件互信息辨识方法，利用混沌理论中的关联积分估计条件互信息，统计量的显著性由置换检验确定．仿真结果证实了方法的有效性，并利用该方法研究了空气污染指标以及中国股市间的Grangerl因果关系．%The Granger Causality is an important basis for measuring the dynamic relationships among system vari- ables. Traditional two-variable Granger causality analysis method is prone to inducing spurious causal relationship and can not portray the immediate causal relationship. This paper explores how to use graphical models method to analyze the Granger causal relations among components of multivariate time series. Granger causality graph of time series is presented. The structural identification of Granger causality graph is investigated based on the conditional mutual information. The conditional mutual information is estimated using the correlation integral from chaos theory. The significance of the tested statistics is determined with a permutation test. The validity of the proposed method is confirmed by simulations analysis. The Granger causal relationships of the air pollution index and the China＇s stock market are investigated using the proposed method.
Model based feature fusion approach
Schwering, P.B.W.
2001-01-01
In recent years different sensor data fusion approaches have been analyzed and evaluated in the field of mine detection. In various studies comparisons have been made between different techniques. Although claims can be made for advantages for using certain techniques, until now there has been no si
Geometrical approach to fluid models
Kuvshinov, B. N.; Schep, T. J.
1997-01-01
Differential geometry based upon the Cartan calculus of differential forms is applied to investigate invariant properties of equations that describe the motion of continuous media. The main feature of this approach is that physical quantities are treated as geometrical objects. The geometrical notio
Global energy modeling - A biophysical approach
Energy Technology Data Exchange (ETDEWEB)
Dale, Michael
2010-09-15
This paper contrasts the standard economic approach to energy modelling with energy models using a biophysical approach. Neither of these approaches includes changing energy-returns-on-investment (EROI) due to declining resource quality or the capital intensive nature of renewable energy sources. Both of these factors will become increasingly important in the future. An extension to the biophysical approach is outlined which encompasses a dynamic EROI function that explicitly incorporates technological learning. The model is used to explore several scenarios of long-term future energy supply especially concerning the global transition to renewable energy sources in the quest for a sustainable energy system.
The chronic diseases modelling approach
Hoogenveen RT; Hollander AEM de; Genugten MLL van; CCM
1998-01-01
A mathematical model structure is described that can be used to simulate the changes of the Dutch public health state over time. The model is based on the concept of demographic and epidemiologic processes (events) and is mathematically based on the lifetable method. The population is divided over s
Institute of Scientific and Technical Information of China (English)
魏岳嵩; 田铮; 肖艳婷
2012-01-01
Detecting the causal relationships among variables is an important content of time series analysis. In this paper, the causal relationships among variables of structural vector autoregressive model are studied using graphical models, time series causal graph is presented and the structural identification problem of the causal graph is investigated. A three-step procedure is developed to orient the causal direction based on the information theory criteria. The mutual informations and the conditional mutual informations are estimated by the correlation integral. Numerical results demonstrate that the proposed method is able to identify the causal structure of causal graph of structural vector autoregressive model very effectively.%由观测数据确定变量间的因果关系是时间序列分析的重要内容.本文利用图模型方法研究结构向量自回归模型变量间的因果关系,通过时间序列因果图的建立将问题转化为时间序列因果图结构的辨识.基于信息论方法提出了因果性定向的三步准则,利用关联积分估计互信息和条件互信息.模拟结果显示本方法能更有效地辨识结构向量自回归模型因果图的因果结构.
Energy consumption and economic growth in China: A multivariate causality test
Energy Technology Data Exchange (ETDEWEB)
Wang Yuan, E-mail: ywang@nju.edu.cn [State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210093 (China); Wang Yichen; Zhou Jing; Zhu Xiaodong; Lu Genfa [State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210093 (China)
2011-07-15
This study takes a fresh look at the direction of causality between energy consumption and economic growth in China during the period from 1972 to 2006, using a multivariate cointegration approach. Given the weakness associated with the bivariate causality framework, the current study performs a multivariate causality framework by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory. Using the recently developed autoregressive distributed lag (ARDL) bounds testing approach, a long-run equilibrium cointegration relationship has been found to exist between economic growth and the explanatory variables: energy consumption, capital and employment. Empirical results reveal that the long-run parameter of energy consumption on economic growth in China is approximately 0.15, through a long-run static solution of the estimated ARDL model, and that for the short-run is approximately 0.12 by the error correction model. The study also indicates the existence of short-run and long-run causality running from energy consumption, capital and employment to economic growth. The estimation results imply that energy serves as an important source of economic growth, thus more vigorous energy use and economic development strategies should be adopted for China. - Highlights: > Cointegration is only present when real GDP is the dependent variable. >The long-run causality running from energy consumption to economic growth. >China is an energy dependent economy.
人因可靠性分析中的概率因果模型%Probabilistic causal model of human reliability analysis
Institute of Scientific and Technical Information of China (English)
高文宇; 张力
2011-01-01
根据人因失误的机理和特点提出了一个分层的人因可靠性概率因果模型,采用贝叶斯网建立了人因可靠性影响因素之间的因果关系.采用分层方法进行建模,充分利用了条件独立性以降低模型的复杂度,同时分层机制也符合人因可靠性的内在要求.该模型既能满足回溯型分析需求又能满足预测型分析需求.在模型的定量化方面,设计了一个简化可行的模型参数计算方法.该模型可以用于工程化的人因可靠性分析,也可扩展为人因行为理论研究模型.%A proper causal model of human reliability analysis helps to account for human response behavior and lead to a more effective quantitative analysis. In this paper, we have proposed a hierarchical causal model of human reliability in accordance with the underlying reasons of human errors, with Bayesian network being used to build the causal link of all the different factors. And, then, with the hierarchical structure adopted in the model, the factors in each level would be only affected by the factors belonging to the upper levels. In addition, since the hierarchical structure may contribute more to a clear and simplified relationship between the behavior influence factors. Specifically speaking, human reliability is supposed to be affected by some inherent factors, though such inherent factors may also be affected by some external factors. However, the external factors of different groups may prove to be independent of each other. Therefore, it is possible to reduce the computational load greatly. In the above model we have initiated, human inherent factors may include confidence and responsibility, knowledge and experience, psychological stress and working load, fatigue and so on. While confidence and responsibility are usually influenced by such external factors as safety culture, organizational management, and team collaboration, knowledge and experience may be influenced by each one' s
Learning Actions Models: Qualitative Approach
DEFF Research Database (Denmark)
Bolander, Thomas; Gierasimczuk, Nina
2015-01-01
identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power......—they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...
A Unified Approach to Modeling and Programming
DEFF Research Database (Denmark)
Madsen, Ole Lehrmann; Møller-Pedersen, Birger
2010-01-01
of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...
Biased causal inseparable game
Bhattacharya, Some Sankar
2015-01-01
Here we study the \\emph{causal inseparable} game introduced in [\\href{http://www.nature.com/ncomms/journal/v3/n10/full/ncomms2076.html}{Nat. Commun. {\\bf3}, 1092 (2012)}], but it's biased version. Two separated parties, Alice and Bob, generate biased bits (say input bit) in their respective local laboratories. Bob generates another biased bit (say decision bit) which determines their goal: whether Alice has to guess Bob's bit or vice-verse. Under the assumption that events are ordered with respect to some global causal relation, we show that the success probability of this biased causal game is upper bounded, giving rise to \\emph{biased causal inequality} (BCI). In the \\emph{process matrix} formalism, which is locally in agreement with quantum physics but assume no global causal order, we show that there exist \\emph{inseparable} process matrices that violate the BCI for arbitrary bias in the decision bit. In such scenario we also derive the maximal violation of the BCI under local operations involving tracele...
Causality discovery technology
Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.
2012-11-01
Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.
Model of Conceptual Change for INQPRO: A Bayesian Network Approach
Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn
2013-01-01
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Four-dimensional Causal Dynamical Triangulations and an effective transfer matrix
Görlich, Andrzej
2013-01-01
Causal Dynamical Triangulations is a background independent approach to quantum gravity. We show that there exists an effective transfer matrix labeled by the scale factor which properly describes the evolution of the quantum universe. In this framework no degrees of freedom are frozen, but, the obtained effective action agrees with the minisuperspace model.
Institute of Scientific and Technical Information of China (English)
闫海; 彭晨
2012-01-01
Causality is one of the elements of civil liability securities misrepresentation. Chinese judicial interpretation of civil liability of securities misrepresentation not only learn from successful experience of the U.S. legislative and judicial, but also should introduce theoretical framework of bifurcated approach to causality. Transaction causation is a causal relationship between securities transactions by investors and misrepresentation, and it should be taken to identify trust presumption. If investors trade on their own to recover damages,they also prove loss causation that a causal relationship between trading losses of investors and misrepresentation. Identification of loss causation can take a direct consequence and make shifting of burden of evidence, as well as other factors such as systematic risk securities to be excluded.%因果关系是证券虚假陈述民事责任的构成要件之一。我国司法解释不仅借鉴美国虚假陈述民事责任立法与司法中较为成功的经验，还应当引入因果关系两分法的理论架构。证券虚假陈述民事责任中的交易因果关系是指投资者的证券交易行为与虚假陈述之间存在因果关系，认定交易因果关系应当采取信赖推定的方法。投资者如果就自己的交易损失予以追偿，在认定交易因果关系的基础上，还必须证明交易损失与虚假陈述行为之间存在因果关系，即损失因果关系。损失因果关系的认定可以采取直接后果并进行举证倒置，以及对证券市场系统风险等其他因素予以排除。
Directory of Open Access Journals (Sweden)
Pogozhina I.N.,
2016-12-01
Full Text Available The possibility of referring of the psychological theories studying interrelation of training and mental development processes to this or that stage of scientific knowledge formation on the basis of studied objects types and corresponded determination systems as a basic criterion distinguishing the ideals of scientific rationality is justified. General characteristics of classical, non-classical and post-non-classical models, determination of the mechanisms of dissipative systems, requirements for learning and development model building in the context of post-non-classic science paradigm on the criterion of the system features of the object of cognition are described. Domestic psychological school models are compared with associanism, behaviorism, gestalt psychology and Piaget determination models on the number of options allocated to these determinants, types of causal chains and types of links between causal chains. It is shown that cultural-historical approach is situated intermediately between post-non-classical and non-classical models, while activity approach corresponds to post-non-classical understanding of the object of study as complicated self-developing "man-size" system. Determination relationships models developed by L.V.Vygotskii, S.L. Rubinstein, A.N. Leont’ev continue to play the heuristic role at the present stage of scientific development.
Arrighi, Pablo
2012-01-01
We generalize the theory of Cellular Automata to arbitrary, time-varying graphs. In other words we formalize, and prove theorems about, the intuitive idea of a labelled graph which evolves in time - but under the natural constraint that information can only ever be transmitted at a bounded speed, with respect to the distance given by the graph. The notion of translation-invariance is also generalized. The definition we provide for these `causal graph dynamics' is simple and axiomatic. The theorems we provide also show that it is robust. For instance, causal graph dynamics are stable under composition and under restriction to radius one. In the finite case some fundamental facts of Cellular Automata theory carry through: causal graph dynamics admit a characterization as continuous functions and they are stable under inversion. The provided examples suggest a wide range of applications of this mathematical object, from complex systems science to theoretical physics. Keywords: Dynamical networks, Boolean network...
Dhakal, K; Tiezzi, F; Clay, J S; Maltecca, C
2015-04-01
Health disorders in dairy cows have a substantial effect on the profitability of a dairy enterprise because of loss in milk sales, culling of unhealthy cows, and replacement costs. Complex relationships exist between health disorders and production traits. Understanding the causal structures among these traits may help us disentangle these complex relationships. The principal objective of this study was to use producer-recorded data to explore phenotypic and genetic relationships among reproductive and metabolic health disorders and production traits in first-lactation US Holsteins. A total of 77,004 first-lactation daughters' records of 2,183 sires were analyzed using recursive models. Health data contained information on reproductive health disorders [retained placenta (RP); metritis (METR)] and metabolic health disorders [ketosis (KETO); displaced abomasum (DA)]. Production traits included mean milk yield (MY) from early lactation (mean MY from 6 to 60 d in milk and from 61 to 120 d in milk), peak milk yield (PMY), day in milk of peak milk yield (PeakD), and lactation persistency (LP). Three different sets of traits were analyzed in which recursive effects from each health disorder on culling, recursive effects of one health disorder on another health disorder and on MY, and recursive effects of each health disorder on production traits, including PeakD, PMY, and LP, were assumed. Different recursive Gaussian-threshold and threshold models were implemented in a Bayesian framework. Estimates of the structural coefficients obtained between health disorders and culling were positive; on the liability scale, the structural coefficients ranged from 0.929 to 1.590, confirming that the presence of a health disorder increased culling. Positive recursive effects of RP to METR (0.117) and of KETO to DA (0.122) were estimated, whereas recursive effects from health disorders to production traits were negligible in all cases. Heritability estimates of health disorders ranged
Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David
2014-01-01
Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length τ, i.e., the GC value is a function of τ. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of τ, which exhibits (i) oscillations, often vanishing at certain finite sampling interval lengths, (ii) the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and non-linear dynamics: the GC value may vanish in the presence of true causal influence or become non-zero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally, we present an approach to circumvent these sampling artifacts to obtain reliable GC values.
Directory of Open Access Journals (Sweden)
Douglas eZhou
2014-07-01
Full Text Available Granger causality (GC is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length $tau$, emph{i.e.}, the GC value is a function of $tau$. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of $tau$, which exhibits (i oscillations, often vanishing at certain finite sampling interval lengths, (ii the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and nonlinear dynamics: the GC value may vanish in the presence of true causal influence or become nonzero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally, we present an approach to circumvent these sampling artifacts to obtain reliable GC values.
Institute of Scientific and Technical Information of China (English)
孙伟; 邵国青; 刘茂军; 武昱孜; 张旭; 华利忠
2013-01-01
利用顶点赋权反馈图分析法分析江苏某猪场沼气系统工程的效益,并建立猪场排泄物无污染的仿真学模型.根据2011年江苏某猪场与年猪粪尿和沼气效益有关的顶点赋权反馈图对其中的权值进行量化,采用量化结果和农户液化气消耗情况及耕地面积,建立猪场排泄物无污染的仿真学模型.结果表明:2011年该系统中含有3条正反馈环(沼气能源效益正反馈环、施肥面积正反馈环和沼渣效益正反馈环)及2条负反馈环(沼气浪费负反馈环和沼肥浪费负反馈环).建立了两套排泄物无污染的仿真学模型的调整方案:一是在平均存栏量(1728头)不变的情况下与168户农户建立输气管道,供农户使用;二是按比例扩大规模至3721头,并建立与周边所有361户农户的输气管道,进一步健全沼液灌溉渠,扩大沼液灌溉面积.%The benefit using biogas engineering system in a pig farm in Jiangsu Province was analyzed with vertex weighted causal loop diagram analysis approach after and a simulation model was established to solve the problem of pollution.According to the vertex weighted causal loop diagram analysis related to annual pig feces and biogas benefit of a pig farm in Jiangsu Province in 2011,the weightings related to the diagram were calculated.Using the results of weightings,the agricultural acreage and the liquefied gas consumption,the simulation model was established.The results showed that there were three positive feedback loops including the biogas benefit,fertilized area and benefit of biogas residues,and two negative feedback loops which were biogas waste and biogas manure waste in the system in 2011.The adjustment schemes of two simulation models were established to solve the problem of pollution:one was to build gas pipeline with 168 households if the amount of livestock (1728) was not changed,the other one was to build gas pipeline with 361 households if the amount of livestock increased
Complementarity, causality, and explanation
Losee, John
2013-01-01
Prior to the work of Niels Bohr, discussions on the relationship of cause and effect presupposed that successful causal attribution implies explanation. The success of quantum theory challenged this presupposition. In this succinct review of the history of these discussions, John Losee presents the philosophical background of debates over the cause-effect relation. He reviews the positions of Aristotle, René Descartes, Isaac Newton, David Hume, Immanuel Kant, and John Stuart Mill. He shows how nineteenth-century theories in physics and chemistry were informed by a dominant theory of causality
Matrix Model Approach to Cosmology
Chaney, A; Stern, A
2015-01-01
We perform a systematic search for rotationally invariant cosmological solutions to matrix models, or more specifically the bosonic sector of Lorentzian IKKT-type matrix models, in dimensions $d$ less than ten, specifically $d=3$ and $d=5$. After taking a continuum (or commutative) limit they yield $d-1$ dimensional space-time surfaces, with an attached Poisson structure, which can be associated with closed, open or static cosmologies. For $d=3$, we obtain recursion relations from which it is possible to generate rotationally invariant matrix solutions which yield open universes in the continuum limit. Specific examples of matrix solutions have also been found which are associated with closed and static two-dimensional space-times in the continuum limit. The solutions provide for a matrix resolution of cosmological singularities. The commutative limit reveals other desirable features, such as a solution describing a smooth transition from an initial inflation to a noninflationary era. Many of the $d=3$ soluti...
Szekeres models: a covariant approach
Apostolopoulos, Pantelis S
2016-01-01
We exploit the 1+1+2 formalism to covariantly describe the inhomogeneous and anisotropic Szekeres models. It is shown that an \\emph{average scale length} can be defined \\emph{covariantly} which satisfies a 2d equation of motion driven from the \\emph{effective gravitational mass} (EGM) contained in the dust cloud. The contributions to the EGM are encoded to the energy density of the dust fluid and the free gravitational field $E_{ab}$. In addition the notions of the Apparent and Absolute Apparent Horizons are briefly discussed and we give an alternative gauge-invariant form to define them in terms of the kinematical variables of the spacelike congruences. We argue that the proposed program can be used in order to express the Sachs optical equations in a covariant form and analyze the confrontation of a spatially inhomogeneous irrotational overdense fluid model with the observational data.
Learning Actions Models: Qualitative Approach
DEFF Research Database (Denmark)
Bolander, Thomas; Gierasimczuk, Nina
2015-01-01
—they are identifiable in the limit.We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning...... methods suited for finite identifiability of particular types of deterministic actions....
Predictions of the causal entropic principle for environmental conditions of the universe
Cline, James M; Holder, Gilbert
2007-01-01
The causal entropic principle has been proposed as a superior alternative to the anthropic principle for understanding the magnitude of the cosmological constant. In this approach, the probability to create observers is assumed to be proportional to the entropy production \\Delta S in a maximal causally connected region -- the causal diamond. We improve on the original treatment by better quantifying the entropy production due to stars, using an analytic model for the star formation history which accurately accounts for changes in cosmological parameters. We calculate the dependence of \\Delta S on the density contrast Q=\\delta\\rho/\\rho, and find that our universe is much closer to the most probable value of Q than in the usual anthropic approach and that probabilities are relatively weakly dependent on this amplitude. In addition, we make first estimates of the dependence of \\Delta S on the baryon fraction and overall matter abundance. Finally, we also explore the possibility that decays of dark matter, sugges...
The Causality between Government Revenue and Government Expenditure in Iran
Elyasi, Yousef; Rahimi, Mohammad
2012-01-01
The causal relationship between government revenue and government expenditure is an important subject in public economics especially to the control of budget deficit. The purpose of this study is to investigate the relationship between government revenue and government expenditure in Iran by applying the bounds testing approach to cointegration. The results of the causality test show that there is a bidirectional causal relationship between government expenditure and revenues in both long run...
Modeling software behavior a craftsman's approach
Jorgensen, Paul C
2009-01-01
A common problem with most texts on requirements specifications is that they emphasize structural models to the near exclusion of behavioral models-focusing on what the software is, rather than what it does. If they do cover behavioral models, the coverage is brief and usually focused on a single model. Modeling Software Behavior: A Craftsman's Approach provides detailed treatment of various models of software behavior that support early analysis, comprehension, and model-based testing. Based on the popular and continually evolving course on requirements specification models taught by the auth
Ghasemi, Mahdieh; Mahloojifar, Ali
2013-04-01
Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD.
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Challenges in structural approaches to cell modeling.
Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A
2016-07-31
Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.
Distributed simulation a model driven engineering approach
Topçu, Okan; Oğuztüzün, Halit; Yilmaz, Levent
2016-01-01
Backed by substantive case studies, the novel approach to software engineering for distributed simulation outlined in this text demonstrates the potent synergies between model-driven techniques, simulation, intelligent agents, and computer systems development.
Understanding Causal Coherence Relations
Mulder, G.
2008-01-01
The research reported in this dissertation focuses on the cognitive processes and representations involved in understanding causal coherence relations in text. Coherence relations are the meaning relations between the information units in the text, such as Cause-Consequence. These relations can be m
Causality: Physics and Philosophy
Chatterjee, Atanu
2013-01-01
Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…
Marsh, Herbert W; Chanal, Julien P; Sarrazin, Philippe G
2006-01-01
A large body of research in support of the reciprocal effects model of causal ordering demonstrates that prior academic self-concept predicts subsequent academic achievement beyond what can be explained in terms of prior achievement. Here we evaluate the generalizability of this support for the reciprocal effects model to a physical activity context in which achievement is reflected in gymnastics skills on a standardized gymnastics performance test evaluated by expert judges. Based on the responses of 376 adolescents collected at the start (T1) and end (T2) of a gymnastics training programme, there is support for a reciprocal effects model in which there are significant paths leading from both T1 gymnastics self-concept to T2 gymnastics skills and from T1 gymnastics skills to T2 self-concept. Although there were gender and age effects (girls and older participants had better gymnastics skills, boys had higher self-concepts), multiple group structural equation models indicated that support for the reciprocal effects model generalized over responses by boys and girls. In summary, self-concept and performance are both determinants and consequences of each other.
Lu, Qing; Li, Haoran; Luo, Guoping; Wang, Yi; Tang, Hao; Han, Li; Yao, Zhijian
2012-08-15
Depression is proved to be associated with the dysfunction of prefrontal-limbic neural circuit, especially during emotion processing procedure. Related explorations have been undertaken from the aspects of abnormal activation and functional connectivity. However, the mechanism of the dysfunction of coordinated interactions remains unknown and is still a matter of debate. The present study gave direct evidence of this issue from the aspect of effective connectivity via dynamic causal modeling (DCM). 20 major depressive disorder (MDD) patients and 20 healthy controls were recruited to attend facial emotional stimulus during MEG recording. Bayesian model selection (BMS) was applied to choose the best model. Results under the optimal model showed that top-down endogenous effective connectivity from the dorsolateral prefrontal cortex (DLPFC) to the amygdala was greatly impaired in patients relative to health controls; while bottom-up endogenous effective connectivity from the amygdala to the anterior cingulate cortex (ACC) as well as modulatory effective connectivity from ACC to DLPFC was significantly increased. We inferred the incapable DLPFC failed to exert influence on amygdala, and finally lead to enhanced amygdala-ACC and ACC-DLPFC bottom-up effects. Such impaired prefrontal-amygdala connectivity was supposed to be responsible for the dysfunction in MDD when dealing with emotional stimuli.
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.
Granger causality and transfer entropy are equivalent for Gaussian variables
Barnett, Lionel; Seth, Anil
2009-01-01
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. It has always seemed plausible that the two concepts ought to be related. 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.
Horvath, C.; Leeflang, P.S.H.; Otter, P.W.
2002-01-01
Dynamic multivariate models ha e become popular in analyzing the behavior of competitive marketing systems because they are capable of incorporating all the relationships in a competitive marketing environment. In this paper we consider VAR models, the most frequently used dynamic multivariate model
A Set Theoretical Approach to Maturity Models
DEFF Research Database (Denmark)
Lasrado, Lester; Vatrapu, Ravi; Andersen, Kim Normann
2016-01-01
Maturity Model research in IS has been criticized for the lack of theoretical grounding, methodological rigor, empirical validations, and ignorance of multiple and non-linear paths to maturity. To address these criticisms, this paper proposes a novel set-theoretical approach to maturity models ch...
Information causality and noisy computations
Energy Technology Data Exchange (ETDEWEB)
Hsu, Li-Yi [Department of Physics, Chung Yuan Christian University, Chung-li 32023, Taiwan (China); Yu, I-Ching; Lin, Feng-Li [Department of Physics, National Taiwan Normal University, Taipei 116, Taiwan (China)
2011-10-15
We reformulate the information causality in a more general framework by adopting the results of signal propagation and computation in a noisy circuit. In our framework, the information causality leads to a broad class of Tsirelson inequalities. This fact allows us to subject information causality to experimental scrutiny. A no-go theorem for reliable nonlocal computation is also derived. Information causality prevents any physical circuit from performing reliable computations.
Noncommutative geometry, Lorentzian structures and causality
Franco, Nicolas
2014-01-01
The theory of noncommutative geometry provides an interesting mathematical background for developing new physical models. In particular, it allows one to describe the classical Standard Model coupled to Euclidean gravity. However, noncommutative geometry has mainly been developed using the Euclidean signature, and the typical Lorentzian aspects of space-time, the causal structure in particular, are not taken into account. We present an extension of noncommutative geometry \\`a la Connes suitable the for accommodation of Lorentzian structures. In this context, we show that it is possible to recover the notion of causality from purely algebraic data. We explore the causal structure of a simple toy model based on an almost commutative geometry and we show that the coupling between the space-time and an internal noncommutative space establishes a new `speed of light constraint'.
Modeling diffuse pollution with a distributed approach.
León, L F; Soulis, E D; Kouwen, N; Farquhar, G J
2002-01-01
The transferability of parameters for non-point source pollution models to other watersheds, especially those in remote areas without enough data for calibration, is a major problem in diffuse pollution modeling. A water quality component was developed for WATFLOOD (a flood forecast hydrological model) to deal with sediment and nutrient transport. The model uses a distributed group response unit approach for water quantity and quality modeling. Runoff, sediment yield and soluble nutrient concentrations are calculated separately for each land cover class, weighted by area and then routed downstream. The distributed approach for the water quality model for diffuse pollution in agricultural watersheds is described in this paper. Integrating the model with data extracted using GIS technology (Geographical Information Systems) for a local watershed, the model is calibrated for the hydrologic response and validated for the water quality component. With the connection to GIS and the group response unit approach used in this paper, model portability increases substantially, which will improve non-point source modeling at the watershed scale level.
Smith, Jason F; Chen, Kewei; Pillai, Ajay S; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define "effective connectivity" using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.
Directory of Open Access Journals (Sweden)
Jason Fitzgerald Smith
2013-05-01
Full Text Available The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here we explicitly define effective connectivity using a common set of observation and state equations that are appropriate for three connectivity methods: Dynamic Causal Modeling (DCM, Multivariate Autoregressive Modeling (MAR, and Switching Linear Dynamic Systems for fMRI (sLDSf. In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.
Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258
MODULAR APPROACH WITH ROUGH DECISION MODELS
Directory of Open Access Journals (Sweden)
Ahmed T. Shawky
2012-09-01
Full Text Available Decision models which adopt rough set theory have been used effectively in many real world applications.However, rough decision models suffer the high computational complexity when dealing with datasets ofhuge size. In this research we propose a new rough decision model that allows making decisions based onmodularity mechanism. According to the proposed approach, large-size datasets can be divided intoarbitrary moderate-size datasets, then a group of rough decision models can be built as separate decisionmodules. The overall model decision is computed as the consensus decision of all decision modulesthrough some aggregation technique. This approach provides a flexible and a quick way for extractingdecision rules of large size information tables using rough decision models.
Modular Approach with Rough Decision Models
Directory of Open Access Journals (Sweden)
Ahmed T. Shawky
2012-10-01
Full Text Available Decision models which adopt rough set theory have been used effectively in many real world applications.However, rough decision models suffer the high computational complexity when dealing with datasets ofhuge size. In this research we propose a new rough decision model that allows making decisions based onmodularity mechanism. According to the proposed approach, large-size datasets can be divided intoarbitrary moderate-size datasets, then a group of rough decision models can be built as separate decisionmodules. The overall model decision is computed as the consensus decision of all decision modulesthrough some aggregation technique. This approach provides a flexible and a quick way for extractingdecision rules of large size information tables using rough decision models.
Pitalúa-García, Damián
2012-01-01
How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combin...
Institute of Scientific and Technical Information of China (English)
周玉华
2012-01-01
内在化的研究路径借用心灵哲学的方法,以近乎反思的模式探求翻译活动的内在过程与其特征。＂因果＂与＂功能＂是研究中的核心要素,其中＂因果＂是译者主体性发挥的途径,＂功能＂指通过社会赋予方式对＂因果＂施加的限制。两者的结合形成了译者主体性发挥的作用链条：＂功能＂——译者——意向（＂因＂）——翻译行为（＂果＂）。%Internalized approach in translation studies aims at uncovering the internal process of translation practice. Within the process, "causality" and "function" are the two core factors, the former demonstrating the translator' s subjective role and the latter manifesting the restrictions that social institution exerts on causality. Working together, they reveal the way in which translator' s subjective role is performed: ty(cause)--tanslation behavior(result)".
On Measurement Bias in Causal Inference
Pearl, Judea
2012-01-01
This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias-free effect estimates in such models.
Stormwater infiltration trenches: a conceptual modelling approach.
Freni, Gabriele; Mannina, Giorgio; Viviani, Gaspare
2009-01-01
In recent years, limitations linked to traditional urban drainage schemes have been pointed out and new approaches are developing introducing more natural methods for retaining and/or disposing of stormwater. These mitigation measures are generally called Best Management Practices or Sustainable Urban Drainage System and they include practices such as infiltration and storage tanks in order to reduce the peak flow and retain part of the polluting components. The introduction of such practices in urban drainage systems entails an upgrade of existing modelling frameworks in order to evaluate their efficiency in mitigating the impact of urban drainage systems on receiving water bodies. While storage tank modelling approaches are quite well documented in literature, some gaps are still present about infiltration facilities mainly dependent on the complexity of the involved physical processes. In this study, a simplified conceptual modelling approach for the simulation of the infiltration trenches is presented. The model enables to assess the performance of infiltration trenches. The main goal is to develop a model that can be employed for the assessment of the mitigation efficiency of infiltration trenches in an integrated urban drainage context. Particular care was given to the simulation of infiltration structures considering the performance reduction due to clogging phenomena. The proposed model has been compared with other simplified modelling approaches and with a physically based model adopted as benchmark. The model performed better compared to other approaches considering both unclogged facilities and the effect of clogging. On the basis of a long-term simulation of six years of rain data, the performance and the effectiveness of an infiltration trench measure are assessed. The study confirmed the important role played by the clogging phenomenon on such infiltration structures.
Non-parametric causal inference for bivariate time series
McCracken, James M
2015-01-01
We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.
Computer Use, Confidence, Attitudes, and Knowledge: A Causal Analysis.
Levine, Tamar; Donitsa-Schmidt, Smadar
1998-01-01
Introduces a causal model which links measures of computer experience, computer-related attitudes, computer-related confidence, and perceived computer-based knowledge. The causal model suggests that computer use has a positive effect on perceived computer self-confidence, as well as on computer-related attitudes. Questionnaires were administered…
Causality-imposed (Kramers-Kronig) relationships between attenuation and dispersion.
Waters, Kendall R; Mobley, Joel; Miller, James G
2005-05-01
Causality imposes restrictions on both the time-domain and frequency-domain responses of a system. The Kramers-Kronig (K-K) relations relate the real and imaginary parts of the frequency-domain response. In ultrasonics, K-K relations often are used to link attenuation and dispersion. We review both integral and differential forms of the frequency-domain K-K relations that are relevant to theoretical models and laboratory measurements. We consider two methods for implementing integral K-K relations for the case of finite-bandwidth data, namely, extrapolation of data and restriction of integration limits. For the latter approach, we discuss the accuracy of K-K predictions for specific classes of system behavior and how the truncation of the integrals affects this accuracy. We demonstrate the accurate prediction of attenuation and dispersion using several forms of the K-K relations relevant to experimental measurements of media with attenuation coefficients obeying a frequency power law and media consisting of resonant scatterers. We also review the time-causal relations that describe the time-domain consequences of causality in the wave equation. These relations can be thought of as time-domain analogs of the (frequency-domain) K-K relations. Causality-imposed relations, such as the K-K and time-causal relations, provide useful tools for the analysis of measurements and models of acoustic systems.
Liang, X San
2014-01-01
Given two time series, can one tell, in a rigorous and quantitative way, the cause and effect between them? Based on a recently rigorized physical notion namely information flow, we arrive at a concise formula and give this challenging question, which is of wide concern in different disciplines, a positive answer. Here causality is measured by the time rate of change of information flowing from one series, say, X2, to another, X1. The measure is asymmetric between the two parties and, particularly, if the process underlying X1 does not depend on X2, then the resulting causality from X2 to X1 vanishes. The formula is tight in form, involving only the commonly used statistics, sample covariances. It has been validated with touchstone series purportedly generated with one-way causality. It has also been applied to the investigation of real world problems; an example presented here is the cause-effect relation between two climate modes, El Ni\\~no and Indian Ocean Dipole, which have been linked to the hazards in f...
Causal viscous cosmology without singularities
Laciana, Carlos E
2016-01-01
An isotropic and homogeneous cosmological model with a source of dark energy is studied. That source is simulated with a viscous relativistic fluid with minimal causal correction. In this model the restrictions on the parameters coming from the following conditions are analized: a) energy density without singularities along time, b) scale factor increasing with time, c) universe accelerated at present time, d) state equation for dark energy with "w" bounded and close to -1. It is found that those conditions are satified for the following two cases. i) When the transport coefficient ({\\tau}_{{\\Pi}}), associated to the causal correction, is negative, with the aditional restriction {\\zeta}|{\\tau}_{{\\Pi}}|>2/3, where {\\zeta} is the relativistic bulk viscosity coefficient. The state equation is in the "phantom" energy sector. ii) For {\\tau}_{{\\Pi}} positive, in the "k-essence" sector. It is performed an exact calculation for the case where the equation of state is constant, finding that option (ii) is favored in r...
Building Water Models, A Different Approach
Izadi, Saeed; Onufriev, Alexey V
2014-01-01
Simplified, classical models of water are an integral part of atomistic molecular simulations, especially in biology and chemistry where hydration effects are critical. Yet, despite several decades of effort, these models are still far from perfect. Presented here is an alternative approach to constructing point charge water models - currently, the most commonly used type. In contrast to the conventional approach, we do not impose any geometry constraints on the model other than symmetry. Instead, we optimize the distribution of point charges to best describe the "electrostatics" of the water molecule, which is key to many unusual properties of liquid water. The search for the optimal charge distribution is performed in 2D parameter space of key lowest multipole moments of the model, to find best fit to a small set of bulk water properties at room temperature. A virtually exhaustive search is enabled via analytical equations that relate the charge distribution to the multipole moments. The resulting "optimal"...
Sladky, Ronald; Höflich, Anna; Küblböck, Martin; Kraus, Christoph; Baldinger, Pia; Moser, Ewald; Lanzenberger, Rupert; Windischberger, Christian
2015-04-01
Social anxiety disorder (SAD) is characterized by over-reactivity of fear-related circuits in social or performance situations and associated with marked social impairment. We used dynamic causal modeling (DCM), a method to evaluate effective connectivity, to test our hypothesis that SAD patients would exhibit dysfunctions in the amygdala-prefrontal emotion regulation network. Thirteen unmedicated SAD patients and 13 matched healthy controls performed a series of facial emotion and object discrimination tasks while undergoing fMRI. The emotion-processing network was identified by a task-related contrast and motivated the selection of the right amygdala, OFC, and DLPFC for DCM analysis. Bayesian model averaging for DCM revealed abnormal connectivity between the OFC and the amygdala in SAD patients. In healthy controls, this network represents a negative feedback loop. In patients, however, positive connectivity from OFC to amygdala was observed, indicating an excitatory connection. As we did not observe a group difference of the modulatory influence of the FACE condition on the OFC to amygdala connection, we assume a context-independent reduction of prefrontal control over amygdalar activation in SAD patients. Using DCM, it was possible to highlight not only the neuronal dysfunction of isolated brain regions, but also the dysbalance of a distributed functional network.
Directory of Open Access Journals (Sweden)
Jose Cristiano Pereira
2015-01-01
Full Text Available The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single model that can be used by decision makers to identify critical risk factors in order to allocate resources to improve the safety of the system. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.
The balanced scorecard: an incremental approach model to health care management.
Pineno, Charles J
2002-01-01
The balanced scorecard represents a technique used in strategic management to translate an organization's mission and strategy into a comprehensive set of performance measures that provide the framework for implementation of strategic management. This article develops an incremental approach for decision making by formulating a specific balanced scorecard model with an index of nonfinancial as well as financial measures. The incremental approach to costs, including profit contribution analysis and probabilities, allows decisionmakers to assess, for example, how their desire to meet different health care needs will cause changes in service design. This incremental approach to the balanced scorecard may prove to be useful in evaluating the existence of causality relationships between different objective and subjective measures to be included within the balanced scorecard.
Evaluating diabetes and hypertension disease causality using mouse phenotypes
Directory of Open Access Journals (Sweden)
Han Jing-Dong J
2010-07-01
Full Text Available Abstract Background Genome-wide association studies (GWAS have found hundreds of single nucleotide polymorphisms (SNPs associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used. Results Here we tapped the rich resource of mouse phenotype data and developed a method to quantify the probability that a gene perturbation causes the phenotypes of a disease. Using type II diabetes (T2D and hypertension (HT as study cases, we found that the genes, when perturbed, having high probability to cause T2D and HT phenotypes tend to be hubs in the interactome networks and are enriched for signaling pathways regulating metabolism but not metabolic pathways, even though the genes in these metabolic pathways are often the most significantly changed in expression levels in these diseases. Conclusions Compared to human genetic disease-based predictions, our mouse phenotype based predictors greatly increased the coverage while keeping a similarly high specificity. The disease phenotype probabilities given by our approach can be used to evaluate the likelihood of disease causality of disease-associated genes and genes surrounding disease-associated SNPs.
Correlation analysis and causality test between Ludong-Huanghai block and South Japan
Institute of Scientific and Technical Information of China (English)
ZHENG Jian-chang; JIANG Hai-kun
2007-01-01
In this paper, we make a comparative analysis and correlation test for the seismic activities in the South Japan and the Ludong-Huanghai block (a secondary tectonic unit in the North China) and approach the relationship between the energy release processes of these two areas by using co-integration analysis and Granger causality test for the time series of random variables. The results show that the seismic activities in these two areas are correlative and synchronous to a certain extent, and their release series of cumulative strain energy are contemporaneously correlative. Both energy series are first-order difference stationary processes and there is secular and steady co-integration between them. We make a positive analysis on the first-order difference energy series through Granger causality test based on vector error correction (VEC) model and find there is unilateral Granger causality and prominent co-integration between the two energy release processes.
Scholl, Joep H G; van de Ven, Peter M; van Puijenbroek, Eugène P
2015-01-01
OBJECTIVES: The aim of this study was to investigate whether the time to onset (TTO) of common adverse drug reactions (ADRs) of antidiabetic drugs could be modeled using parametric distributions and whether these TTO distributions were dependent on patient characteristics. Furthermore, information r
Graham, Carroll M.; Scott, Aaron J.; Nafukho, Fredrick M.
2008-01-01
While theoretical models aimed at explaining or predicting employee turnover outcomes have been developed, minimal consideration has been given to the same task regarding safety, often measured as the probability of a crash in a given time frame. The present literature review identifies four constructs from turnover literature, which are believed…
Sysoeva, M.V.; Sitnikova, E.Y.; Sysoev, I.V.; Bezruchko, B.P.; Luijtelaar, E.L.J.M. van
2014-01-01
Background: Advanced methods of signal analysis of the preictal and ictal activity dynamics characterizing absence epilepsy in humans with absences and in genetic animal models have revealed new and unknown electroencephalographic characteristics, that has led to new insights and theories. New metho
A message-passing approach for recurrent-state epidemic models on networks
Shrestha, Munik; Moore, Cristopher
2015-01-01
Epidemic processes are common out-of-equilibrium phenomena of broad interdisciplinary interest. Recently, dynamic message-passing (DMP) has been proposed as an efficient algorithm for simulating epidemic models on networks, and in particular for estimating the probability that a given node will become infectious at a particular time. To date, DMP has been applied exclusively to models with one-way state changes, as opposed to models like SIS (susceptible-infectious-susceptible) and SIRS (susceptible-infectious-recovered-susceptible) where nodes can return to previously inhabited states. Because many real-world epidemics can exhibit such recurrent dynamics, we propose a DMP algorithm for complex, recurrent epidemic models on networks. Our approach takes correlations between neighboring nodes into account while preventing causal signals from backtracking to their immediate source, and thus avoids "echo chamber effects" where a pair of adjacent nodes each amplify the probability that the other is infectious. We ...
Modelling Coagulation Systems: A Stochastic Approach
Ryazanov, V V
2011-01-01
A general stochastic approach to the description of coagulating aerosol system is developed. As the object of description one can consider arbitrary mesoscopic values (number of aerosol clusters, their size etc). The birth-and-death formalism for a number of clusters can be regarded as a partial case of the generalized storage model. An application of the storage model to the number of monomers in a cluster is discussed.
Modelling the Causal Relationship between Seniority of the CEO in the Enterprise and the Debt in USA
Directory of Open Access Journals (Sweden)
Chafik Kammoun
2012-04-01
Full Text Available This paper develops a model in which the interaction of Seniority of the C.E.O in theenterprise and the debt can be analyzed. Multiple securities arise as optimal in the model. This allowsfor a meaningful analysis of interaction effects between Seniority of the C.E.O in the enterprise andthe debt for a panel of USA firms from 2000 to 2009. There is a predicted (positive relationshipbetween Seniority of the C.E.O in the enterprise and the debt. Finally, this paper uses the recentdevelopments in the econometrics of non-stationarydynamic panels to reassess the relationshipbetween Seniority of the C.E.O in the enterprise and the debt
A Multiple Model Approach to Modeling Based on LPF Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.``
Post-16 Biology--Some Model Approaches?
Lock, Roger
1997-01-01
Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)
Hishinuma, Earl S; Chang, Janice Y; McArdle, John J; Hamagami, Fumiaki
2012-09-01
There is a relatively consistent negative relationship between adolescent depressive symptoms and educational achievement (e.g., grade point average [GPA]). However, the causal direction for this association is less certain due to the lack of longitudinal data with both indicators measured across at least 2 time periods and due to the lack of application of more sophisticated contemporary statistical techniques. We present multivariate results from a large longitudinal cohort-sequential study of high school students (N = 7,317) with measures of self-reported depressive symptoms and self-reported GPAs across multiple time points (following McArdle, 2009, and McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001) using an ethnically diverse sample from Hawai'i. Contemporary statistical techniques included bivariate dynamic structural equation modeling (DSEM), multigroup ethnic and gender DSEMs, ordinal scale measurement of key outcomes, and imputation for incomplete longitudinal data. The findings suggest that depressive symptoms affect subsequent academic achievement and not the other way around, especially for Native Hawaiians compared with female non-Hawaiians. We further discuss the scientific, applied, and methodological-statistical implications of the results, including the need for further theorizing and research on mediating variables. We also discuss the need for increased prevention, early intervention, screening, identification, and treatment of depressive symptoms and disorders. Finally, we argue for utilization of more contemporary methodological-statistical techniques, especially when violating parametric test assumptions.
Energy Technology Data Exchange (ETDEWEB)
Kumar Mandal, Sabuj, E-mail: sabujecon@gmail.co [T.A. Pai Management Institute (TAPMI), Manipal 576104, Karnataka (India); Madheswaran, S. [Institute for Social and Economic Change, Bangalore 560072, Karnataka (India)
2010-11-15
The aim of this paper is to examine the existence and direction of the causal relationship between energy consumption and output growth in the Indian cement industry for the period 1979-80 to 2004-05. The most recently developed panel unit root, a heterogeneous panel cointegration and panel-based error correction model, is applied within a multivariate framework. The empirical results confirm a positive, long-run cointegrated relationship between output and energy consumption when heterogeneous state effects are taken into account. We also found a long-run, bi-directional relationship between energy consumption and output growth in the Indian cement industry for the study period, implying that an increase in energy consumption directly affects the growth of this sector and that growth stimulates further energy consumption. These empirical findings imply that energy consumption and output are jointly determined and affect each other. The empirical evidence also suggests the implementation of energy conservation policies oriented toward improving energy-use efficiency to avoid any negative impacts of the conservation policies on the growth of this industry.
Energy Technology Data Exchange (ETDEWEB)
Kumar Mandal, Sabuj [T.A. Pai Management Institute (TAPMI), Manipal 576104, Karnataka (India); Madheswaran, S. [Institute for Social and Economic Change, Bangalore 560072, Karnataka (India)
2010-11-15
The aim of this paper is to examine the existence and direction of the causal relationship between energy consumption and output growth in the Indian cement industry for the period 1979-80 to 2004-05. The most recently developed panel unit root, a heterogeneous panel cointegration and panel-based error correction model, is applied within a multivariate framework. The empirical results confirm a positive, long-run cointegrated relationship between output and energy consumption when heterogeneous state effects are taken into account. We also found a long-run, bi-directional relationship between energy consumption and output growth in the Indian cement industry for the study period, implying that an increase in energy consumption directly affects the growth of this sector and that growth stimulates further energy consumption. These empirical findings imply that energy consumption and output are jointly determined and affect each other. The empirical evidence also suggests the implementation of energy conservation policies oriented toward improving energy-use efficiency to avoid any negative impacts of the conservation policies on the growth of this industry. (author)
Decomposition approach to model smart suspension struts
Song, Xubin
2008-10-01
Model and simulation study is the starting point for engineering design and development, especially for developing vehicle control systems. This paper presents a methodology to build models for application of smart struts for vehicle suspension control development. The modeling approach is based on decomposition of the testing data. Per the strut functions, the data is dissected according to both control and physical variables. Then the data sets are characterized to represent different aspects of the strut working behaviors. Next different mathematical equations can be built and optimized to best fit the corresponding data sets, respectively. In this way, the model optimization can be facilitated in comparison to a traditional approach to find out a global optimum set of model parameters for a complicated nonlinear model from a series of testing data. Finally, two struts are introduced as examples for this modeling study: magneto-rheological (MR) dampers and compressible fluid (CF) based struts. The model validation shows that this methodology can truly capture macro-behaviors of these struts.
Application of transfer entropy to causality detection and synchronization experiments in tokamaks
Murari, A.; Peluso, E.; Gelfusa, M.; Garzotti, L.; Frigione, D.; Lungaroni, M.; Pisano, F.; Gaudio, P.; Contributors, JET
2016-02-01
Determination of causal-effect relationships can be a difficult task even in the analysis of time series. This is particularly true in the case of complex, nonlinear systems affected by significant levels of noise. Causality can be modelled as a flow of information between systems, allowing to better predict the behaviour of a phenomenon on the basis of the knowledge of the one causing it. Therefore, information theoretic tools, such as the transfer entropy, have been used in various disciplines to quantify the causal relationship between events. In this paper, Transfer Entropy is applied to determining the information relationship between various phenomena in Tokamaks. The proposed approach provides unique insight about information causality in difficult situations, such as the link between sawteeth and ELMs and ELM pacing experiments. The application to the determination of disruption causes, and therefore to the classification of disruption types, looks also very promising. The obtained results indicate that the proposed method can provide a quantitative and statistically sound criterion to address the causal-effect relationships in various difficult and ambiguous situations if the data is of sufficient quality.
Howard, Eric M
2016-01-01
We analyze spacetimes with horizons and study the thermodynamic aspects of causal horizons, suggesting that the resemblance between gravitational and thermodynamic systems has a deeper quantum mechanical origin. We find that the observer dependence of such horizons is a direct consequence of associating a temperature and entropy to a spacetime. The geometrical picture of a horizon acting as a one-way membrane for information flow can be accepted as a natural interpretation of assigning a quantum field theory to a spacetime with boundary, ultimately leading to a close connection with thermodynamics.
Quantum information causality.
Pitalúa-García, Damián
2013-05-24
How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs.
Causality and Micro-Causality in Curved Spacetime
Hollowood, Timothy J.; Shore, Graham M.
2007-01-01
We consider how causality and micro-causality are realised in QED in curved spacetime. The photon propagator is found to exhibit novel non-analytic behaviour due to vacuum polarization, which invalidates the Kramers-Kronig dispersion relation and calls into question the validity of micro-causality in curved spacetime. This non-analyticity is ultimately related to the generic focusing nature of congruences of geodesics in curved spacetime, as implied by the null energy condition, and the exist...
Directory of Open Access Journals (Sweden)
Cheng-tao Yu
2014-10-01
Full Text Available The purpose of this study is to examine the relationship between Total Quality Management (TQM practices, quality capabilities, competitiveness and firm performance. In this study, TQM has been conceptualized as soft and hard practices. An empirical analysis based upon an extensive validation process was applied to refine the construct scales, respectively. The sample consists of 423 valid responses for applying Structural Equation Modeling (SEM. Results derived from this study show that soft TQM practices have a direct, positive and significant relationship between quality capabilities, competitive strategies and Organizational performance. In addition, an indirect, positive and significant relationship on organizational performance through quality capabilities and competitive strategies was observed. The findings of this research show that hypotheses H3b, H4b and H6b do not support, the rest are in line with the model inference. Particularly, from the results indicate that soft TQM are the most important resource, which has strong effects on organizational performance. Results derived from this study might help managers to implement TQM practices in order to effectively allocate resources and improve financial performance. Thus, managers should consider that improvement in soft TQM would support the successful implementation of quality capabilities, competitive advantage and organizational performance. Much efforts relating to social aspects in TQM activities are particularly key issues to improve performance.
Beaudoin, Christopher E; Chen, Hongliang; Agha, Sohail
2016-01-01
Rapid population growth in Pakistan poses major risks, including those pertinent to public health. In the context of family planning in Pakistan, the current study evaluates the Touch condom media campaign and its effects on condom-related awareness, attitudes, behavioral intention, and behavior. This evaluation relies on 3 waves of panel survey data from men married to women ages 15-49 living in urban and rural areas in Pakistan (N = 1,012): Wave 1 was March 15 to April 7, 2009; Wave 2 was August 10 to August 24, 2009; and Wave 3 was May 1 to June 13, 2010. Analysis of variance provided evidence of improvements in 10 of 11 condom-related outcomes from Wave 1 to Wave 2 and Wave 3. In addition, there was no evidence of outcome decay 1 year after the conclusion of campaign advertising dissemination. To help compensate for violating the assumption of random assignment, propensity score modeling offered evidence of the beneficial effects of confirmed Touch ad recall on each of the 11 outcomes in at least 1 of 3 time-lagged scenarios. By using these different time-lagged scenarios (i.e., from Wave 1 to Wave 2, from Wave 1 to Wave 3, and from Wave 2 to Wave 3), propensity score modeling permitted insights into how the campaign had time-variant effects on the different types of condom-related outcomes, including carryover effects of the media campaign.
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.
Diabetes: the layperson's theories of causality.
Mercado-Martinez, Francisco J; Ramos-Herrera, Igor Martin
2002-07-01
The authors examine laypersons' perspectives of illness: the content of causal explanations of diabetes and differences in explanations according to gender. Qualitative research was carried out in Guadalajara, Mexico. A nonprobabilistic sample of 20 diabetic individuals participated in interviews, and the content of the interviews was analyzed. On the origin of their condition, participants offered explanations that match neither the biomedical model nor any other formal causal theory. Participants attributed the onset of diabetes to socioemotional circumstances linked to their life experiences and practices. Men attributed causality to work and social circumstances outside the home; women attributed it to family life and domestic circumstances. The authors discuss how lay theories can be useful for the reorganization of health services.
[Clinical research III. The causality studies].
Talavera, Juan O; Wacher-Rodarte, Niels H; Rivas-Ruiz, Rodolfo
2011-01-01
The need to solve a clinical problem leads us to establish a starting point to address (risk, prognosis or treatment studies), all these cases seek to attribute causality. Clinical reasoning described in the book Clinical Epidemiology. The architecture of clinical research, offers a simple guide to understanding this phenomenon. And proposes three basic components: baseline, maneuver and outcome. In this model, different systematic errors (bias) are described, which may be favored by omitting characteristics of the three basic components. Thus, omissions in the baseline characteristics cause an improper assembly of the population and susceptibility bias, omissions in the application or evaluation of the maneuver provoke performance bias, and omissions in the assessment of out-come cause detection bias and transfer bias. Importantly, if this way of thinking facilitates understanding of the causal phenomenon, the appropriateness of the variables to be selected in the studies to which attribute or not causality, require additional arguments for evaluate clinical relevance.
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...... injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses....
Plewan, Thorsten; Weidner, Ralph; Eickhoff, Simon B; Fink, Gereon R
2012-10-01
The human visual system converts identically sized retinal stimuli into different-sized perceptions. For instance, the Müller-Lyer illusion alters the perceived length of a line via arrows attached to its end. The strength of this illusion can be expressed as the difference between physical and perceived line length. Accordingly, illusion strength reflects how strong a representation is transformed along its way from a retinal image up to a conscious percept. In this study, we investigated changes of effective connectivity between brain areas supporting these transformation processes to further elucidate the neural underpinnings of optical illusions. The strength of the Müller-Lyer illusion was parametrically modulated while participants performed either a spatial or a luminance task. Lateral occipital cortex and right superior parietal cortex were found to be associated with illusion strength. Dynamic causal modeling was employed to investigate putative interactions between ventral and dorsal visual streams. Bayesian model selection indicated that a model that involved bidirectional connections between dorsal and ventral stream areas most accurately accounted for the underlying network dynamics. Connections within this network were partially modulated by illusion strength. The data further suggest that the two areas subserve differential roles: Whereas lateral occipital cortex seems to be directly related to size transformation processes, activation in right superior parietal cortex may reflect subsequent levels of processing, including task-related supervisory functions. Furthermore, the data demonstrate that the observer's top-down settings modulate the interactions between lateral occipital and superior parietal regions and thereby influence the effect of illusion strength.
Heat transfer modeling an inductive approach
Sidebotham, George
2015-01-01
This innovative text emphasizes a "less-is-more" approach to modeling complicated systems such as heat transfer by treating them first as "1-node lumped models" that yield simple closed-form solutions. The author develops numerical techniques for students to obtain more detail, but also trains them to use the techniques only when simpler approaches fail. Covering all essential methods offered in traditional texts, but with a different order, Professor Sidebotham stresses inductive thinking and problem solving as well as a constructive understanding of modern, computer-based practice. Readers learn to develop their own code in the context of the material, rather than just how to use packaged software, offering a deeper, intrinsic grasp behind models of heat transfer. Developed from over twenty-five years of lecture notes to teach students of mechanical and chemical engineering at The Cooper Union for the Advancement of Science and Art, the book is ideal for students and practitioners across engineering discipl...
K-causal structure of space-time in general relativity
Indian Academy of Sciences (India)
Sujatha Janardhan; R V Saraykar
2008-04-01
Using K-causal relation introduced by Sorkin and Woolgar [1], we generalize results of Garcia-Parrado and Senovilla [2,3] on causal maps. We also introduce causality conditions with respect to K-causality which are analogous to those in classical causality theory and prove their inter-relationships. We introduce a new causality condition following the work of Bombelli and Noldus [4] and show that this condition lies in between global hyperbolicity and causal simplicity. This approach is simpler and more general as compared to traditional causal approach [5,6] and it has been used by Penrose et al [7] in giving a new proof of positivity of mass theorem. 0-space-time structures arise in many mathematical and physical situations like conical singularities, discontinuous matter distributions, phenomena of topology-change in quantum field theory etc.
Causal events enter awareness faster than non-causal events
Wagemans, Johan; de-Wit, Lee
2017-01-01
Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967). Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946). Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS)—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005)—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world. PMID:28149698
Ito, Sosuke
2016-11-01
The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics.
Causal interpretation of stochastic differential equations
DEFF Research Database (Denmark)
Sokol, Alexander; Hansen, Niels Richard
2014-01-01
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....
Inductive Reasoning about Causally Transmitted Properties
Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D.; Tenenbaum, Joshua B.
2008-01-01
Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates'…
Comments: Causal Interpretations of Mediation Effects
Jo, Booil; Stuart, Elizabeth A.
2012-01-01
The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…
The Feasibility of Using Causal Indicators in Educational Measurement
Wang, Jue; Engelhard, George, Jr.
2016-01-01
The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…
From Blickets to Synapses: Inferring Temporal Causal Networks by Observation
Fernando, Chrisantha
2013-01-01
How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we…
Causal Relationship Between Relative Price Variability and Inflation in Turkey:
Directory of Open Access Journals (Sweden)
Nebiye Yamak
2016-09-01
Full Text Available This study investigates the causal relationship between inflation and relative price variability in Turkey for the period of January 2003-January 2014, by using panel data. In the study, a Granger (1969 non-causality test in heterogeneous panel data models developed by Dumitrescu and Hurlin (2012 is utilized to determine the causal relations between inflation rate relative price variability. The panel data consists of 4123 observations: 133 time observations and 31 cross-section observations. The results of panel causality test indicate that there is a bidirectional causality between inflation rate and relative price variability by not supporting the imperfection information model of Lucas and the menu cost model of Ball and Mankiw.
Quantum objects as elementary units of causality and locality
Diel, Hans H
2016-01-01
The author's attempt to construct a local causal model of quantum theory (QT) that includes quantum field theory (QFT) resulted in the identification of "quantum objects" as the elementary units of causality and locality. Quantum objects are collections of particles (including single particles) whose collective dynamics and measurement results can only be described by the laws of QT and QFT. Local causal models of quantum objects' internal dynamics are not possible if a locality is understood as a space-point locality. Within quantum objects, state transitions may occur which instantly affect the whole quantum object. The identification of quantum objects as the elementary units of causality and locality has two primary implications for a causal model of quantum objects: (1) quantum objects run autonomously with system-state update frequencies based on their local proper times and with either no or minimal dependency on external parameters. (2) The laws of physics that describe global (but relativistic) inter...
A Bayesian Shrinkage Approach for AMMI Models.
da Silva, Carlos Pereira; de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves; Balestre, Marcio
2015-01-01
Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior
A Bayesian Shrinkage Approach for AMMI Models.
Directory of Open Access Journals (Sweden)
Carlos Pereira da Silva
Full Text Available Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI model, are widely applicable to genotype-by-environment interaction (GEI studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05 in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct
Causal evolution of wave packets
Eckstein, Michał
2016-01-01
Drawing from the optimal transport theory adapted to the relativistic setting we formulate the principle of a causal flow of probability and apply it in the wave packet formalism. We demonstrate that whereas the Dirac system is causal, the relativistic-Schr\\"odinger Hamiltonian impels a superluminal evolution of probabilities. We quantify the causality breakdown in the latter system and argue that, in contrast to the popular viewpoint, it is not related to the localisation properties of the states.
Integrating functional data to prioritize causal variants in statistical fine-mapping studies.
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Gleb Kichaev
2014-10-01
Full Text Available Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy. Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.
Causality in non-commutative quantum field theories
Energy Technology Data Exchange (ETDEWEB)
Haque, Asrarul; Joglekar, Satish D [Department of Physics, I.I.T. Kanpur, Kanpur 208 016 (India)], E-mail: ahaque@iitk.ac.in, E-mail: sdj@iitk.ac.in
2008-05-30
We study causality in noncommutative quantum field theory with a space-space noncommutativity. We employ the S operator approach of Bogoliubov-Shirkov (BS). We generalize the BS criterion of causality to the noncommutative theory. The criterion to test causality leads to a nonzero difference between the T* product and the T product as a condition of causality violation for a spacelike separation. We discuss two examples; one in a scalar theory and another in the Yukawa theory. In particular, in the context of a noncommutative Yukawa theory, with the interaction Lagrangian {psi}-bar(x)*{psi}(x)*{phi}(x), is observed to be causality violating even in the case of space-space noncommutativity for which {theta}{sup 0i} = 0.
The Framework, Causal and Co-compact Structure of Space-time
Kovár, Martin
2013-01-01
We introduce a canonical, compact topology, which we call weakly causal, naturally generated by the causal site of J. D. Christensen and L. Crane, a pointless algebraic structure motivated by certain problems of quantum gravity. We show that for every four-dimensional globally hyperbolic Lorentzian manifold there exists an associated causal site, whose weakly causal topology is co-compact with respect to the manifold topology and vice versa. Thus, the causal site has the full information about the topology of space-time, represented by the Lorentzian manifold. In addition, we show that there exist also non-Lorentzian causal sites (whose causal relation is not a continuous poset) and so the weakly causal topology and its de Groot dual extends the usual manifold topology of space-time beyond topologies generated by the traditional, smooth model. As a source of inspiration in topologizing the studied causal structures, we use some methods and constructions of general topology and formal concept analysis.
World oil and agricultural commodity prices: Evidence from nonlinear causality
Energy Technology Data Exchange (ETDEWEB)
Nazlioglu, Saban, E-mail: snazlioglu@pau.edu.t [Department of Econometrics, Pamukkale University, Denizli (Turkey)
2011-05-15
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: {yields} This study determines the price transmission mechanisms between the world oil and three key agricultural commodity prices (corn, soybeans, and wheat). {yields} The linear and nonlinear cointegration and causality methods are carried out. {yields} The linear causality analysis supports evidence on the neutrality hypothesis. {yields} The nonlinear causality analysis shows that there is a persistent unidirectional causality from the oil prices to the corn and to the soybeans prices.
Scientific Theories, Models and the Semantic Approach
Directory of Open Access Journals (Sweden)
Décio Krause
2007-12-01
Full Text Available According to the semantic view, a theory is characterized by a class of models. In this paper, we examine critically some of the assumptions that underlie this approach. First, we recall that models are models of something. Thus we cannot leave completely aside the axiomatization of the theories under consideration, nor can we ignore the metamathematics used to elaborate these models, for changes in the metamathematics often impose restrictions on the resulting models. Second, based on a parallel between van Fraassen’s modal interpretation of quantum mechanics and Skolem’s relativism regarding set-theoretic concepts, we introduce a distinction between relative and absolute concepts in the context of the models of a scientific theory. And we discuss the significance of that distinction. Finally, by focusing on contemporary particle physics, we raise the question: since there is no general accepted unification of the parts of the standard model (namely, QED and QCD, we have no theory, in the usual sense of the term. This poses a difficulty: if there is no theory, how can we speak of its models? What are the latter models of? We conclude by noting that it is unclear that the semantic view can be applied to contemporary physical theories.
Yermolayeva, Yevdokiya; Rakison, David H
2014-01-01
Connectionist models have been applied to many phenomena in infant development including perseveration, language learning, categorization, and causal perception. In this article, we discuss the benefits of connectionist networks for the advancement of theories of early development. In particular, connectionist models contribute novel testable predictions, instantiate the theorized mechanism of change, and create a unifying framework for understanding infant learning and development. We relate these benefits to the 2 primary approaches used in connectionist models of infant development. The first approach employs changes in neural processing as the basis for developmental changes, and the second employs changes in infants' experiences. The review sheds light on the unique hurdles faced by each approach as well as the challenges and solutions related to both, particularly with respect to the identification of critical model components, parameter specification, availability of empirical data, and model comparison. Finally, we discuss the future of modeling work as it relates to the study of development. We propose that connectionist networks stand to make a powerful contribution to the generation and revision of theories of early child development. Furthermore, insights from connectionist models of early development can improve the understanding of developmental changes throughout the life span.
Multiscale Model Approach for Magnetization Dynamics Simulations
De Lucia, Andrea; Tretiakov, Oleg A; Kläui, Mathias
2016-01-01
Simulations of magnetization dynamics in a multiscale environment enable rapid evaluation of the Landau-Lifshitz-Gilbert equation in a mesoscopic sample with nanoscopic accuracy in areas where such accuracy is required. We have developed a multiscale magnetization dynamics simulation approach that can be applied to large systems with spin structures that vary locally on small length scales. To implement this, the conventional micromagnetic simulation framework has been expanded to include a multiscale solving routine. The software selectively simulates different regions of a ferromagnetic sample according to the spin structures located within in order to employ a suitable discretization and use either a micromagnetic or an atomistic model. To demonstrate the validity of the multiscale approach, we simulate the spin wave transmission across the regions simulated with the two different models and different discretizations. We find that the interface between the regions is fully transparent for spin waves with f...
Causal Inference from Big Data: Theoretical Foundations and the Data-fusion Problem
2015-06-01
big data | confounding | external validity | meta-analysis | heterogeneity | selection bias | data integration Introduction – Causal Inference and...the next section as part of the Structural Causal Model ( SCM ) framework. One unique feature of the SCM framework, essential in big data applications...defining structural causal models ( SCMs ) and stating the two fundamental laws of causal inference. We then consider re- spectively the problem of
Continuum modeling an approach through practical examples
Muntean, Adrian
2015-01-01
This book develops continuum modeling skills and approaches the topic from three sides: (1) derivation of global integral laws together with the associated local differential equations, (2) design of constitutive laws and (3) modeling boundary processes. The focus of this presentation lies on many practical examples covering aspects such as coupled flow, diffusion and reaction in porous media or microwave heating of a pizza, as well as traffic issues in bacterial colonies and energy harvesting from geothermal wells. The target audience comprises primarily graduate students in pure and applied mathematics as well as working practitioners in engineering who are faced by nonstandard rheological topics like those typically arising in the food industry.
Interfacial Fluid Mechanics A Mathematical Modeling Approach
Ajaev, Vladimir S
2012-01-01
Interfacial Fluid Mechanics: A Mathematical Modeling Approach provides an introduction to mathematical models of viscous flow used in rapidly developing fields of microfluidics and microscale heat transfer. The basic physical effects are first introduced in the context of simple configurations and their relative importance in typical microscale applications is discussed. Then,several configurations of importance to microfluidics, most notably thin films/droplets on substrates and confined bubbles, are discussed in detail. Topics from current research on electrokinetic phenomena, liquid flow near structured solid surfaces, evaporation/condensation, and surfactant phenomena are discussed in the later chapters. This book also: Discusses mathematical models in the context of actual applications such as electrowetting Includes unique material on fluid flow near structured surfaces and phase change phenomena Shows readers how to solve modeling problems related to microscale multiphase flows Interfacial Fluid Me...
The Cradle of Causal Reasoning: Newborns' Preference for Physical Causality
Mascalzoni, Elena; Regolin, Lucia; Vallortigara, Giorgio; Simion, Francesca
2013-01-01
Perception of mechanical (i.e. physical) causality, in terms of a cause-effect relationship between two motion events, appears to be a powerful mechanism in our daily experience. In spite of a growing interest in the earliest causal representations, the role of experience in the origin of this sensitivity is still a matter of dispute. Here, we…
Spread of entanglement and causality
Casini, Horacio; Liu, Hong; Mezei, Márk
2016-07-01
We investigate causality constraints on the time evolution of entanglement entropy after a global quench in relativistic theories. We first provide a general proof that the so-called tsunami velocity is bounded by the speed of light. We then generalize the free particle streaming model of [1] to general dimensions and to an arbitrary entanglement pattern of the initial state. In more than two spacetime dimensions the spread of entanglement in these models is highly sensitive to the initial entanglement pattern, but we are able to prove an upper bound on the normalized rate of growth of entanglement entropy, and hence the tsunami velocity. The bound is smaller than what one gets for quenches in holographic theories, which highlights the importance of interactions in the spread of entanglement in many-body systems. We propose an interacting model which we believe provides an upper bound on the spread of entanglement for interacting relativistic theories. In two spacetime dimensions with multiple intervals, this model and its variations are able to reproduce intricate results exhibited by holographic theories for a significant part of the parameter space. For higher dimensions, the model bounds the tsunami velocity at the speed of light. Finally, we construct a geometric model for entanglement propagation based on a tensor network construction for global quenches.
Yoon, Ju Young; Brown, Roger L
2014-01-01
Cross-lagged panel analysis (CLPA) is a method of examining one-way or reciprocal causal inference between longitudinally changing variables. It has been used in the social sciences for many years, but not much in nursing research. This article introduces the conceptual and statistical background of CLPA and provides an exemplar of CLPA that examines the reciprocal causal relationship between depression and cognitive function over time in older adults. The 2-year cross-lagged effects of depressive symptoms (T1) on cognitive function (T2) and cognitive function (T1) on depressive symptoms (T2) were significant, which demonstrated a reciprocal causal relationship between cognitive function and depressive mood over time. Although CLPA is a methodologically strong approach to examine the reciprocal causal inferences over time, it is necessary to consider potential sources of spuriousness to lead to false causal relationship and a reasonable time frame to detect the change of the variables.
Evolutionary modeling-based approach for model errors correction
Wan, S. Q.; He, W. P.; Wang, L.; Jiang, W.; Zhang, W.
2012-08-01
The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data." On the basis of the intelligent features of evolutionary modeling (EM), including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
Runge, Jakob
2016-04-01
Causal reconstruction techniques from multivariate time series have become a popular approach to analyze interactions in complex systems such as the Earth. These approaches allow to exclude effects of common drivers and indirect influences. Practical applications are, however, especially challenging if nonlinear interactions are taken into account and for typically strongly autocorrelated climate time series. Here we discuss a new reconstruction approach with accompanying software package (Tigramite) and focus on two applications: (1) Information or perturbation transfer along causal pathways. This method allows to detect and quantify which intermediate nodes are important mediators of an interaction mechanism and is illustrated to disentangle pathways of atmospheric flow over Europe and for the ENSO - Indian Monsoon interaction mechanism. (2) A nonlinear model-free prediction technique that efficiently utilizes causal drivers and can be shown to yield information-theoretically optimal predictors avoiding over-fitting. The performance of this framework is illustrated on a climatological index of El Nino Southern Oscillation. References: Runge, J. (2015). Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 062829. doi:10.1103/PhysRevE.92.062829 Runge, J., Donner, R. V., & Kurths, J. (2015). Optimal model-free prediction from multivariate time series. Phys. Rev. E, 91(5), 052909. doi:10.1103/PhysRevE.91.052909 Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., … Kurths, J. (2015). Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. doi:10.1038/ncomms9502
Inferring deterministic causal relations
Daniusis, Povilas; Mooij, Joris; Zscheischler, Jakob; Steudel, Bastian; Zhang, Kun; Schoelkopf, Bernhard
2012-01-01
We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.
Bastos-Leite, António J; Ridgway, Gerard R; Silveira, Celeste; Norton, Andreia; Reis, Salomé; Friston, Karl J
2015-01-01
We report the first stochastic dynamic causal modeling (sDCM) study of effective connectivity within the default mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophrenia--according to the Diagnostic and Statistic Manual of Mental Disorders, 4th edition, revised criteria--were studied. Fifteen healthy control subjects (4 women, mean age = 24.6 years, SD = 4) were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI) interspersed with 2 periods of continuous picture viewing. The anterior frontal (AF), posterior cingulate (PC), and the left and right parietal nodes of the DMN were localized in an unbiased fashion using data from 16 independent healthy volunteers (using an identical fMRI protocol). We used sDCM to estimate directed connections between and within nodes of the DMN, which were subsequently compared with t tests at the between subject level. The excitatory effect of the PC node on the AF node and the inhibitory self-connection of the AF node were significantly weaker in patients (mean values = 0.013 and -0.048 Hz, SD = 0.09 and 0.05, respectively) relative to healthy subjects (mean values = 0.084 and -0.088 Hz, SD = 0.15 and 0.77, respectively; P < .05). In summary, sDCM revealed reduced effective connectivity to the AF node of the DMN--reflecting a reduced postsynaptic efficacy of prefrontal afferents--in patients with first-episode schizophrenia.
Directory of Open Access Journals (Sweden)
Long-Biao eCui
2015-11-01
Full Text Available Understanding the neural basis of schizophrenia (SZ is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC, dorsolateral prefrontal cortex (DLPFC, hippocampus, and medial prefrontal cortex (MPFC have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI. Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA in addition to classical inference (t-test. In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, sDCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions.
Hypothesizing and Refining Causal Models,
1984-12-01
and giuided by many peo pl,. I would like to thank: IPattick Winst.on, my tesis supervisor, for showing me how to do research by vxamniple Ihow lse...systems. CHAPTER 2 RE.ISENI’ING IIiYSICAl, SYSI’NMS AND TIIEIM CIIAN(QES The input to the learning program is English text. This text substitutes for
Causal Reasoning with Mental Models
2014-08-08
are learned from knowledge by 568 acquaintance, others from knowledge by description. You cannot acquire the full concept of a color, 569 a wine , or...27 Salsburg, D. (2001). The lady tasting tea: How statistics revolutionized science in the twentieth 1031 century. New York: W.H. Freeman. 1032
Causal Poisson bracket via deformation quantization
Berra-Montiel, Jasel; Molgado, Alberto; Palacios-García, César D.
2016-06-01
Starting with the well-defined product of quantum fields at two spacetime points, we explore an associated Poisson structure for classical field theories within the deformation quantization formalism. We realize that the induced star-product is naturally related to the standard Moyal product through an appropriate causal Green’s functions connecting points in the space of classical solutions to the equations of motion. Our results resemble the Peierls-DeWitt bracket that has been analyzed in the multisymplectic context. Once our star-product is defined, we are able to apply the Wigner-Weyl map in order to introduce a generalized version of Wick’s theorem. Finally, we include some examples to explicitly test our method: the real scalar field, the bosonic string and a physically motivated nonlinear particle model. For the field theoretic models, we have encountered causal generalizations of the creation/annihilation relations, and also a causal generalization of the Virasoro algebra for the bosonic string. For the nonlinear particle case, we use the approximate solution in terms of the Green’s function, in order to construct a well-behaved causal bracket.
Regularization of turbulence - a comprehensive modeling approach
Geurts, B. J.
2011-12-01
Turbulence readily arises in numerous flows in nature and technology. The large number of degrees of freedom of turbulence poses serious challenges to numerical approaches aimed at simulating and controlling such flows. While the Navier-Stokes equations are commonly accepted to precisely describe fluid turbulence, alternative coarsened descriptions need to be developed to cope with the wide range of length and time scales. These coarsened descriptions are known as large-eddy simulations in which one aims to capture only the primary features of a flow, at considerably reduced computational effort. Such coarsening introduces a closure problem that requires additional phenomenological modeling. A systematic approach to the closure problem, know as regularization modeling, will be reviewed. Its application to multiphase turbulent will be illustrated in which a basic regularization principle is enforced to physically consistently approximate momentum and scalar transport. Examples of Leray and LANS-alpha regularization are discussed in some detail, as are compatible numerical strategies. We illustrate regularization modeling to turbulence under the influence of rotation and buoyancy and investigate the accuracy with which particle-laden flow can be represented. A discussion of the numerical and modeling errors incurred will be given on the basis of homogeneous isotropic turbulence.
A causal examination of the effects of confounding factors on multimetric indices
Schoolmaster, Donald R.; Grace, James B.; Schweiger, E. William; Mitchell, Brian R.; Guntenspergen, Glenn R.
2013-01-01
The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.
Neural Correlates of Causal Power Judgments
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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.
Griffiths, Thomas L.; Tenenbaum, Joshua B.
2009-01-01
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…