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Sample records for granger-geweke causality modeling

  1. Dynamic Granger-Geweke causality modeling with application to interictal spike propagation.

    Science.gov (United States)

    Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W; Stufflebeam, Steven M; Hämäläinen, Matti S

    2009-06-01

    A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using structural equation modeling (SEM) and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested that the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. (c) 2009 Wiley-Liss, Inc.

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

    Science.gov (United States)

    Solo, Victor

    2016-05-01

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

  3. Causally nonseparable processes admitting a causal model

    International Nuclear Information System (INIS)

    Feix, Adrien; Araújo, Mateus; Brukner, Caslav

    2016-01-01

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

  4. Causal Reasoning with Mental Models

    Science.gov (United States)

    2014-08-08

    mreasoner/. 445 In broad terms, three strands of evidence corroborate the model theory of causal deductions. The 446 first strand of evidence bears ...models and causal reasoning Sangeet Khemlani et al. 13 She will not gain weight. 459 Will she not eat protein? 460 The results therefore bear out the... Adele Goldberg, Catrinel Haught, Max Lotstein, Marco Ragni, and Greg 821 Trafton for helpful criticisms. 822 Khemlani et al. Causal reasoning with

  5. Linear causal modeling with structural equations

    CERN Document Server

    Mulaik, Stanley A

    2009-01-01

    Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

  6. Causality

    Science.gov (United States)

    Pearl, Judea

    2000-03-01

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

  7. Psychiatric comorbidity and causal disease models.

    Science.gov (United States)

    van Loo, Hanna M; Romeijn, Jan-Willem; de Jonge, Peter; Schoevers, Robert A

    2013-12-01

    In psychiatry, comorbidity is the rule rather than the exception. Up to 45% of all patients are classified as having more than one psychiatric disorder. These high rates of comorbidity have led to a debate concerning the interpretation of this phenomenon. Some authors emphasize the problematic character of the high rates of comorbidity because they indicate absent zones of rarities. Others consider comorbid conditions to be a validator for a particular reclassification of diseases. In this paper we will show that those at first sight contrasting interpretations of comorbidity are based on similar assumptions about disease models. The underlying ideas are that firstly high rates of comorbidity are the result of the absence of causally defined diseases in psychiatry, and second that causal disease models are preferable to non-causal disease models. We will argue that there are good reasons to seek after causal understanding of psychiatric disorders, but that causal disease models will not rule out high rates of comorbidity--neither in psychiatry, nor in medicine in general. By bringing to the fore these underlying assumptions, we hope to clear the ground for a different understanding of comorbidity, and of models for psychiatric diseases. Copyright © 2012 Elsevier Inc. All rights reserved.

  8. 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.

  9. Causal Measurement Models: Can Criticism Stimulate Clarification?

    Science.gov (United States)

    Markus, Keith A.

    2016-01-01

    In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

  10. A Causal Model of Faculty Turnover Intentions.

    Science.gov (United States)

    Smart, John C.

    1990-01-01

    A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…

  11. Diagnostic reasoning using qualitative causal models

    International Nuclear Information System (INIS)

    Sudduth, A.L.

    1992-01-01

    The application of expert systems to reasoning problems involving real-time data from plant measurements has been a topic of much research, but few practical systems have been deployed. One obstacle to wider use of expert systems in applications involving real-time data is the lack of adequate knowledge representation methodologies for dynamic processes. Knowledge bases composed mainly of rules have disadvantages when applied to dynamic processes and real-time data. This paper describes a methodology for the development of qualitative causal models that can be used as knowledge bases for reasoning about process dynamic behavior. These models provide a systematic method for knowledge base construction, considerably reducing the engineering effort required. They also offer much better opportunities for verification and validation of the knowledge base, thus increasing the possibility of the application of expert systems to reasoning about mission critical systems. Starting with the Signed Directed Graph (SDG) method that has been successfully applied to describe the behavior of diverse dynamic processes, the paper shows how certain non-physical behaviors that result from abstraction may be eliminated by applying causal constraint to the models. The resulting Extended Signed Directed Graph (ESDG) may then be compiled to produce a model for use in process fault diagnosis. This model based reasoning methodology is used in the MOBIAS system being developed by Duke Power Company under EPRI sponsorship. 15 refs., 4 figs

  12. A Quantitative Causal Model Theory of Conditional Reasoning

    Science.gov (United States)

    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…

  13. Theories of conduct disorder: a causal modelling analysis

    NARCIS (Netherlands)

    Krol, N.P.C.M.; Morton, J.; Bruyn, E.E.J. De

    2004-01-01

    Background: If a clinician has to make decisions on diagnosis and treatment, he or she is confronted with a variety of causal theories. In order to compare these theories a neutral terminology and notational system is needed. The Causal Modelling framework involving three levels of description –

  14. Causality in 1+1-dimensional Yukawa model-II

    Indian Academy of Sciences (India)

    2013-10-01

    Oct 1, 2013 ... shown that the effective model can be interpreted as a field theory of a bound state. We study causality in such a ... the motivation pertaining to causality violation in the bound states. In §3 condition of .... Consider a diagram with n external scalars, L fermion loops, V vertices, IF internal fermion lines and IB ...

  15. Identification of Mixed Causal-Noncausal Models in Finite Samples

    NARCIS (Netherlands)

    Hecq, Alain; Lieb, Lenard; Telg, Sean

    2016-01-01

    Gouriéroux and Zakoïan (2013) propose to use noncausal models to parsimoniously capture nonlinear features often observed in financial time series and in particular bubble phenomena. In order to distinguish causal autoregressive processes from purely noncausal or mixed causal-noncausal ones, one has

  16. Causal Bayes Model of Mathematical Competence in Kindergarten

    Directory of Open Access Journals (Sweden)

    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.

  17. Inferring Causalities in Landscape Genetics: An Extension of Wright's Causal Modeling to Distance Matrices.

    Science.gov (United States)

    Fourtune, Lisa; Prunier, Jérôme G; Paz-Vinas, Ivan; Loot, Géraldine; Veyssière, Charlotte; Blanchet, Simon

    2018-04-01

    Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic data sets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods-that is, maximum-likelihood path analysis and the directional separation test-by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fits of the tested models. We used an empirical data set combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature, and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.

  18. A Causal Model for Fluctuating Sugar Levels in Diabetes Patients

    Directory of Open Access Journals (Sweden)

    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.

  19. A causal model for fluctuating sugar levels in diabetes patients.

    Science.gov (United States)

    Chhogyal, Kinzang; Nayak, Abhaya; Schwitter, Rolf; Sattar, Abdul

    2012-01-01

    Causal models of physiological systems can be immensely useful in medicine as they may be used for both diagnostic and therapeutic reasoning. 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. 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. 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. 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.

  20. Causal Analysis for Performance Modeling of Computer Programs

    Directory of Open Access Journals (Sweden)

    Jan Lemeire

    2007-01-01

    Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

  1. Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

    Science.gov (United States)

    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

  2. Enhancing scientific reasoning by refining students' models of multivariable causality

    Science.gov (United States)

    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

  3. Chain graph models and their causal interpretations

    DEFF Research Database (Denmark)

    Lauritzen, Steffen Lilholt; Richardson, Thomas S.

    2002-01-01

    , 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...... traditionally been used to model feed-back in econometrics....

  4. Causal Models for Safety Assurance Technologies Project

    Data.gov (United States)

    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...

  5. Interrelations among SMED Stages: A Causal Model

    Directory of Open Access Journals (Sweden)

    José Roberto Díaz-Reza

    2017-01-01

    Full Text Available Mexico has received a lot of foreign investment that has brought in a wide range of novel production philosophies, such as Single Minute Exchange of Dies (SMED. Despite its popularity and reported effectiveness, Mexican companies often quit SMED implementation as they consider it challenging. This usually happens when organizations are not familiarized enough with each one of the SMED stages or do not know how they are interrelated. In this article the interrelations among the different SMED implementation stages by means of a structural equations model are analyzed. Data for constructing the model were gathered from a survey administered to 250 employees from the Mexican maquiladora industry. The survey assessed the importance of 14 activities belonging to the four SMED stages. The descriptive analyses of these stages were conducted and integrated into a structural equations model as latent variables, to find their level of dependency. The model was constructed using WarpPLS 5 software, and direct, indirect, and total effects among variables are analyzed and validated. Results from the model revealed that Stage 1 of SMED implementation, known as the Identification Stage, has both direct and indirect effects on all the other SMED stages, being the most important stage.

  6. Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness.

    Science.gov (United States)

    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.

  7. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Science.gov (United States)

    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

  8. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Directory of Open Access Journals (Sweden)

    Gerald eYoung

    2015-11-01

    Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

  9. Measured, modeled, and causal conceptions of fitness

    Science.gov (United States)

    Abrams, Marshall

    2012-01-01

    This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804

  10. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

    Science.gov (United States)

    Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

    2017-05-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

  11. Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

    Science.gov (United States)

    Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

    2017-01-01

    Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

  12. A hierarchical causal modeling for large industrial plants supervision

    International Nuclear Information System (INIS)

    Dziopa, P.; Leyval, L.

    1994-01-01

    A supervision system has to analyse the process current state and the way it will evolve after a modification of the inputs or disturbance. It is proposed to base this analysis on a hierarchy of models, witch differ by the number of involved variables and the abstraction level used to describe their temporal evolution. In a first step, special attention is paid to causal models building, from the most abstract one. Once the hierarchy of models has been build, the most detailed model parameters are estimated. Several models of different abstraction levels can be used for on line prediction. These methods have been applied to a nuclear reprocessing plant. The abstraction level could be chosen on line by the operator. Moreover when an abnormal process behaviour is detected a more detailed model is automatically triggered in order to focus the operator attention on the suspected subsystem. (authors). 11 refs., 11 figs

  13. A Causal Model of Consumer-Based Brand Equity

    Directory of Open Access Journals (Sweden)

    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.

  14. Spatiotemporal causal modeling for the management of Dengue Fever

    Science.gov (United States)

    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.

  15. mpdcm: A toolbox for massively parallel dynamic causal modeling.

    Science.gov (United States)

    Aponte, Eduardo A; Raman, Sudhir; Sengupta, Biswa; Penny, Will D; Stephan, Klaas E; Heinzle, Jakob

    2016-01-15

    Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs). Simulations are generated in parallel by implementing a low storage explicit Runge-Kutta's scheme on a GPU architecture. mpdcm is publicly available under the GPLv3 license. We found that mpdcm efficiently generates large number of simulations without compromising their accuracy. As applications of mpdcm, we suggest two computationally expensive sampling algorithms: thermodynamic integration and parallel tempering. mpdcm is up to two orders of magnitude more efficient than the standard implementation in the software package SPM. Parallel tempering increases the mixing properties of the traditional Metropolis-Hastings algorithm at low computational cost given efficient, parallel simulations of a model. Future applications of DCM will likely require increasingly large computational resources, for example, when the likelihood landscape of a model is multimodal, or when implementing sampling methods for multi-subject analysis. Due to the wide availability of GPUs, algorithmic advances can be readily available in the absence of access to large computer grids, or when there is a lack of expertise to implement algorithms in such grids. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. The Dynamic Causal Relationship between Electricity Consumption and Economic Growth in Ghana: A Trivariate Causality Model

    Directory of Open Access Journals (Sweden)

    Bernard N. Iyke

    2014-06-01

    Full Text Available This paper examines the dynamic causal relationship between electricity consumption and economic growth in Ghana within a trivariate ARDL framework, for the period 1971–2012.The paper obviates the variable omission bias, and the use of cross-sectional techniques that characterise most existing studies. The results show that there is a distinct causal flow from economic growth to electricity consumption: both in the short run and in the long run. This finding supports the growth-led electricity consumption hypothesis, as documented in the literature. The paper urges policymakers in Ghana to resort to alternative sources of electric power generation, in order to reduce any future pressures on the current sources of electricity production. Appropriate monetary policies must also be put in place, in order to accommodate potential inflation hikes stemming from excessive demands for electricity in the near future.

  17. Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

    DEFF Research Database (Denmark)

    Bouwman, Aniek C; Valente, Bruno D; Janss, Luc L G

    2014-01-01

    Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models...... are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select...

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

    Science.gov (United States)

    Mugan, Jonathan

    2013-05-01

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

  19. Modelling and Analysis of the Dynamics of Adaptive Temporal-Causal Network Models for Evolving Social Interactions

    NARCIS (Netherlands)

    Treur, J.

    2017-01-01

    Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes. Adaptive temporal-causal network models are based on causal relations by which the

  20. Cause and Event: Supporting Causal Claims through Logistic Models

    Science.gov (United States)

    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…

  1. Gradient-based MCMC samplers for dynamic causal modelling.

    Science.gov (United States)

    Sengupta, Biswa; Friston, Karl J; Penny, Will D

    2016-01-15

    In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated using Hamilton's equation of motion and (b) Langevin Monte Carlo algorithm (LMC-R and LMC-E) that simulates the Langevin diffusion of samples using gradients either on a Euclidean (E) or on a Riemannian (R) manifold. While LMC-R requires minimal tuning, the implementation of HMC-E is heavily dependent on its tuning parameters. These parameters are therefore optimised by learning a Gaussian process model of the time-normalised sample correlation matrix. This allows one to formulate an objective function that balances tuning parameter exploration and exploitation, furnishing an intervention-free inference scheme. Using neural mass models (NMMs)-a class of biophysically motivated DCMs-we find that HMC-E is statistically more efficient than LMC-R (with a Riemannian metric); yet both gradient-based samplers are far superior to the random walk Metropolis algorithm, which proves inadequate to steer away from dynamical instability. Copyright © 2015. Published by Elsevier Inc.

  2. Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations

    Directory of Open Access Journals (Sweden)

    Yuan Yang

    2017-09-01

    Full Text Available Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms and late (>100 ms periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG. We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.

  3. Dynamic causal modelling of brain-behaviour relationships.

    Science.gov (United States)

    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). Copyright © 2015 Elsevier Inc. All rights reserved.

  4. Dynamical Causal Modeling from a Quantum Dynamical Perspective

    Science.gov (United States)

    Demiralp, Emre; Demiralp, Metin

    2010-09-01

    Recent research suggests that any set of first order linear vector ODEs can be converted to a set of specific vector ODEs adhering to what we have called "Quantum Harmonical Form (QHF)". QHF has been developed using a virtual quantum multi harmonic oscillator system where mass and force constants are considered to be time variant and the Hamiltonian is defined as a conic structure over positions and momenta to conserve the Hermiticity. As described in previous works, the conversion to QHF requires the matrix coefficient of the first set of ODEs to be a normal matrix. In this paper, this limitation is circumvented using a space extension approach expanding the potential applicability of this method. Overall, conversion to QHF allows the investigation of a set of ODEs using mathematical tools available to the investigation of the physical concepts underlying quantum harmonic oscillators. The utility of QHF in the context of dynamical systems and dynamical causal modeling in behavioral and cognitive neuroscience is briefly discussed.

  5. Causal reasoning and models of cognitive tasks for naval nuclear power plant operators

    International Nuclear Information System (INIS)

    Salazar-Ferrer, P.

    1995-06-01

    In complex industrial process control, causal reasoning appears as a major component in operators' cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs

  6. Causal Agency Theory: Reconceptualizing a Functional Model of Self-Determination

    Science.gov (United States)

    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…

  7. Causal Indicator Models Have Nothing to Do with Measurement

    Science.gov (United States)

    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…

  8. Revisiting Aristotle's causality: model for development in Nigeria ...

    African Journals Online (AJOL)

    This work critically examined the implications of Aristotle‟s theory of causality for development. Aristotle classified the causes of things into four categories: material cause, formal cause, efficient cause and final cause. The question confronting this work is, what is the material causes, formal causes, efficient causes and final ...

  9. Seeing Perfectly Fitting Factor Models That Are Causally Misspecified: Understanding That Close-Fitting Models Can Be Worse

    Science.gov (United States)

    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…

  10. Toward a formalized account of attitudes: The Causal Attitude Network (CAN) Model

    NARCIS (Netherlands)

    Dalege, J.; Borsboom, D.; Harreveld, F. van; Berg, H. van den; Conner, M.; Maas, H.L.J. van der

    2016-01-01

    This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions

  11. Classical Causal Models for Bell and Kochen-Specker Inequality Violations Require Fine-Tuning

    Directory of Open Access Journals (Sweden)

    Eric G. Cavalcanti

    2018-04-01

    Full Text Available Nonlocality and contextuality are at the root of conceptual puzzles in quantum mechanics, and they are key resources for quantum advantage in information-processing tasks. Bell nonlocality is best understood as the incompatibility between quantum correlations and the classical theory of causality, applied to relativistic causal structure. Contextuality, on the other hand, is on a more controversial foundation. In this work, I provide a common conceptual ground between nonlocality and contextuality as violations of classical causality. First, I show that Bell inequalities can be derived solely from the assumptions of no signaling and no fine-tuning of the causal model. This removes two extra assumptions from a recent result from Wood and Spekkens and, remarkably, does not require any assumption related to independence of measurement settings—unlike all other derivations of Bell inequalities. I then introduce a formalism to represent contextuality scenarios within causal models and show that all classical causal models for violations of a Kochen-Specker inequality require fine-tuning. Thus, the quantum violation of classical causality goes beyond the case of spacelike-separated systems and already manifests in scenarios involving single systems.

  12. Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

    OpenAIRE

    Gopnik, Alison; Wellman, Henry M.

    2012-01-01

    We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...

  13. How causal analysis can reveal autonomy in models of biological systems

    Science.gov (United States)

    Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa

    2017-11-01

    Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.

  14. Searching for recursive causal structures in multivariate quantitative genetics mixed models.

    Science.gov (United States)

    Valente, Bruno D; Rosa, Guilherme J M; de Los Campos, Gustavo; Gianola, Daniel; Silva, Martinho A

    2010-06-01

    Biology is characterized by complex interactions between phenotypes, such as recursive and simultaneous relationships between substrates and enzymes in biochemical systems. Structural equation models (SEMs) can be used to study such relationships in multivariate analyses, e.g., with multiple traits in a quantitative genetics context. Nonetheless, the number of different recursive causal structures that can be used for fitting a SEM to multivariate data can be huge, even when only a few traits are considered. In recent applications of SEMs in mixed-model quantitative genetics settings, causal structures were preselected on the basis of prior biological knowledge alone. Therefore, the wide range of possible causal structures has not been properly explored. Alternatively, causal structure spaces can be explored using algorithms that, using data-driven evidence, can search for structures that are compatible with the joint distribution of the variables under study. However, the search cannot be performed directly on the joint distribution of the phenotypes as it is possibly confounded by genetic covariance among traits. In this article we propose to search for recursive causal structures among phenotypes using the inductive causation (IC) algorithm after adjusting the data for genetic effects. A standard multiple-trait model is fitted using Bayesian methods to obtain a posterior covariance matrix of phenotypes conditional to unobservable additive genetic effects, which is then used as input for the IC algorithm. As an illustrative example, the proposed methodology was applied to simulated data related to multiple traits measured on a set of inbred lines.

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

    NARCIS (Netherlands)

    Blankendaal, Romy; Parinussa, Sarah; Treur, Jan

    2016-01-01

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

  16. An Adaptive Temporal-Causal Network Model for Enabling Learning of Social Interaction

    NARCIS (Netherlands)

    Commu, Charlotte; Theelen, Mathilde; Treur, J.

    2017-01-01

    In this study, an adaptive temporal-causal network model is present-ed for learning of basic skills for social interaction. It focuses on greeting a known person and how that relates to learning how to recognize a person from seeing his or her face. The model involves a Hebbian learning process. The

  17. Critical Thinking and Political Participation: The Development and Assessment of a Causal Model.

    Science.gov (United States)

    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…

  18. Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

    Science.gov (United States)

    Lozano, A. C.

    2010-12-01

    Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring

  19. Bayesian nonparametric generative models for causal inference with missing at random covariates.

    Science.gov (United States)

    Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J

    2018-03-26

    We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.

  20. A 2D model of causal set quantum gravity: the emergence of the continuum

    International Nuclear Information System (INIS)

    Brightwell, Graham; Henson, Joe; Surya, Sumati

    2008-01-01

    Non-perturbative theories of quantum gravity inevitably include configurations that fail to resemble physically reasonable spacetimes at large scales. Often, these configurations are entropically dominant and pose an obstacle to obtaining the desired classical limit. We examine this 'entropy problem' in a model of causal set quantum gravity corresponding to a discretization of 2D spacetimes. Using results from the theory of partial orders we show that, in the large volume or continuum limit, its partition function is dominated by causal sets which approximate to a region of 2D Minkowski space. This model of causal set quantum gravity thus overcomes the entropy problem and predicts the emergence of a physically reasonable geometry

  1. Dynamic causal models of neural system dynamics: current state ...

    Indian Academy of Sciences (India)

    Prakash

    2006-09-28

    Sep 28, 2006 ... 3. Principles of DCM. An important limitation of previous methods for determining effective connectivity from functional imaging data, e.g. structural equation modelling (McIntosh and Gonzalez-. Lima 1994; Büchel and Friston 1997) or multivariate autoregressive models (Goebel et al 2003; Harrison et al.

  2. Verification of temporal-causal network models by mathematical analysis

    Directory of Open Access Journals (Sweden)

    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.

  3. Futures Business Models for an IoT Enabled Healthcare Sector: A Causal Layered Analysis Perspective

    OpenAIRE

    Julius Francis Gomes; Sara Moqaddemerad

    2016-01-01

    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...

  4. Capturing cognitive causal paths in human reliability analysis with Bayesian network models

    International Nuclear Information System (INIS)

    Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.

    2017-01-01

    reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC . We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. - Highlights: • A framework for building traceable BNs for HRA, based on cognitive causal paths. • A qualitative BN structure, directly showing these causal paths is developed. • Node reduction algorithms are used for making the BN structure quantifiable. • BN quantified through expert estimates and observed data (Bayesian updating). • The framework is illustrated for a crew failure mode of IDHEAS.

  5. The impact of school leadership on school level factors: validation of a causal model

    NARCIS (Netherlands)

    Krüger, M.L.; Witziers, B.; Sleegers, P.

    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

  6. Causal Modeling of Secondary Science Students' Intentions to Enroll in Physics.

    Science.gov (United States)

    Crawley, Frank E.; Black, Carolyn B.

    1992-01-01

    Reports a study using the causal modeling method to verify underlying causes of student interest in enrolling in physics as predicted by the theory of planned behavior. Families were identified as major referents in the social support system for physics enrollment. Course and extracurricular conflicts and fear of failure were primary beliefs…

  7. Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters

    Science.gov (United States)

    Weisberg, Deena S.; Gopnik, Alison

    2013-01-01

    Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…

  8. Examining a Causal Model of Early Drug Involvement Among Inner City Junior High School Youths.

    Science.gov (United States)

    Dembo, Richard; And Others

    Reflecting the need to construct more inclusive, socially and culturally relevant conceptions of drug use than currently exist, the determinants of drug involvement among inner-city youths within the context of a causal model were investigated. The drug involvement of the Black and Puerto Rican junior high school girls and boys was hypothesized to…

  9. Predicting Adaptive Performance in Multicultural Teams: A Causal Model

    Science.gov (United States)

    2008-02-01

    International Personality Item Pool – Five-Factor Model ( IPIP -FFM), http://ipip.ori.org/, were used in the present study to assess neuroticism as an... IPIP personality scale. Based on Matsumoto et al.’s (2001) results, only those items that exceeded their established criterion for factor loadings... IPIP ) were combined in a composite score representing cultural adjustment (α = .75). As described below, the factor of emotion regulation will be

  10. Concurrency Models with Causality and Events as Psi-calculi

    Directory of Open Access Journals (Sweden)

    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.

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

    International Nuclear Information System (INIS)

    Balcilar, Mehmet; Ozdemir, Zeynel Abidin

    2013-01-01

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

  12. Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.

    Science.gov (United States)

    Faes, Luca; Nollo, Giandomenico; Chon, Ki H

    2008-03-01

    A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of the PI was assessed using a surrogate data technique. The proposed method was tested with simulation examples involving short realizations of linear stochastic processes and nonlinear deterministic signals in which either unidirectional or bidirectional coupling and varying strengths of interactions were imposed. It was found that the OPS-based NARX model was accurate and sensitive in detecting imposed Granger causality conditions. In addition, the OPS-based NARX model was more accurate than the least squares method. Application to the systolic blood pressure and heart rate variability signals demonstrated the feasibility of the method. In particular, we found a bilateral causal relationship between the two signals as evidenced by the significant reduction in the PI values with the NARX model prediction compared to the NAR model prediction, which was also confirmed by the surrogate data analysis. Furthermore, we found significant reduction in the complexity of the dynamics of the two causal pathways of the two signals as the body position was changed from the supine to upright. The proposed is a general method, thus, it can be applied to a wide variety of physiological signals to better understand causality and coupling that may be different between normal and diseased

  13. 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.

  14. 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.

  15. Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

    Science.gov (United States)

    Gopnik, Alison; Wellman, Henry M.

    2012-01-01

    We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

  16. Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

    Science.gov (United States)

    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.

  17. Mendelian Randomization versus Path Models: Making Causal Inferences in Genetic Epidemiology.

    Science.gov (United States)

    Ziegler, Andreas; Mwambi, Henry; König, Inke R

    2015-01-01

    The term Mendelian randomization is popular in the current literature. The first aim of this work is to describe the idea of Mendelian randomization studies and the assumptions required for drawing valid conclusions. The second aim is to contrast Mendelian randomization and path modeling when different 'omics' levels are considered jointly. We define Mendelian randomization as introduced by Katan in 1986, and review its crucial assumptions. We introduce path models as the relevant additional component to the current use of Mendelian randomization studies in 'omics'. Real data examples for the association between lipid levels and coronary artery disease illustrate the use of path models. Numerous assumptions underlie Mendelian randomization, and they are difficult to be fulfilled in applications. Path models are suitable for investigating causality, and they should not be mixed up with the term Mendelian randomization. In many applications, path modeling would be the appropriate analysis in addition to a simple Mendelian randomization analysis. Mendelian randomization and path models use different concepts for causal inference. Path modeling but not simple Mendelian randomization analysis is well suited to study causality with different levels of 'omics' data. 2015 S. Karger AG, Basel.

  18. Mixed Causal-Noncausal AR Processes and the Modelling of Explosive Bubbles

    OpenAIRE

    Fries, Sébastien; Zakoian, Jean-Michel

    2017-01-01

    Noncausal autoregressive models with heavy-tailed errors generate locally explosive processes and therefore provide a natural framework for modelling bubbles in economic and financial time series. We investigate the probability properties of mixed causal-noncausal autoregressive processes, assuming the errors follow a stable non-Gaussian distribution. We show that the tails of the conditional distribution are lighter than those of the errors, and we emphasize the presence of ARCH effects and ...

  19. Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation

    Directory of Open Access Journals (Sweden)

    Paolo Vineis

    2017-06-01

    Full Text Available Abstract In the last decades, Systems Biology (including cancer research has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a causality in epidemiology and in philosophical theorizing—notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

  20. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

    Science.gov (United States)

    Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

    2017-12-28

    Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which

  1. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

    Directory of Open Access Journals (Sweden)

    Yuanyuan Yu

    2017-12-01

    Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal

  2. Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer's disease.

    Science.gov (United States)

    Iturria-Medina, Yasser; Carbonell, Félix M; Sotero, Roberto C; Chouinard-Decorte, Francois; Evans, Alan C

    2017-05-15

    Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-β (Aβ) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aβ) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aβ based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional

  3. [Representations of causality and depression. A factorial approach to the resignation model in the depressed patient].

    Science.gov (United States)

    Comiskey, F; de Bonis, M

    1988-01-01

    The present study investigates causal attributions for stressful life events within the context of Beck's cognitive theory of affective disorders and Seligman's learned helplessness model of depression. The aim was to assess the validity of the depressive attributional style proposed by Seligman, with a clinically depressed population for negative life events. This study presents a factor analysis of the causal attributions of depressed psychiatric in patients measured in relation to one negative life event per subject. The experimental procedure consisted in asking 71 ward depressed patients (51 females and 20 males) to answer 15 items along a seven point scale in order to assess the causes, consequences and control attributed. Statistical treatment using both multidimensional analyses (to describe the dimensions of causality) and univariate comparisons show: 1. The existence of a three dimensional solution, which is interpreted in terms of Seligman's reformulated helplessness model, and which confirms the notion of a "depressive attributional style". 2. A positive relationship between intensity of depression and the tendency to generalize the effects of negative life events (dimension of globality in Seligman's model and generalizability in Beck's). As this relationship is a function of the level of depression it is considered as a psychological state rather than as a personality trait. 3. Inter-sex differences with regard to the attribution of personal versus universal control, with female patients indicating more personal helplessness in relation to others. The results are discussed in relation to epidemiological data and personality theory.

  4. From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology

    Science.gov (United States)

    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.

  5. Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life.

    Science.gov (United States)

    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. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  6. Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour

    Science.gov (United States)

    Lazic, Stanley E.

    2012-01-01

    There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past 15 years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenesis mediates the effect of an intervention on behaviour. Previous studies may have incorrectly attributed changes in behavioural performance to neurogenesis because the role of known (or unknown) neurogenesis-independent mechanisms was not formally taken into consideration during the analysis. Causal models can tease apart complex causal relationships and were used to demonstrate that the effect of exercise on pattern separation is via neurogenesis-independent mechanisms. Many studies in the neurogenesis literature would benefit from the use of statistical methods that can separate neurogenesis-dependent from neurogenesis-independent effects on behaviour. PMID:21957118

  7. Causal reasoning and models of cognitive tasks for naval nuclear power plant operators; Raisonnement causal et modelisation de l`activite cognitive d`operateurs de chaufferie nucleaire navale

    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.

  8. A quantum causal discovery algorithm

    Science.gov (United States)

    Giarmatzi, Christina; Costa, Fabio

    2018-03-01

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

  9. The relationship of family characteristics and bipolar disorder using causal-pie models.

    Science.gov (United States)

    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. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  10. Darwin's diagram of divergence of taxa as a causal model for the origin of species.

    Science.gov (United States)

    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.

  11. 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.

  12. Conditional spectrum computation incorporating multiple causal earthquakes and ground-motion prediction models

    Science.gov (United States)

    Lin, Ting; Harmsen, Stephen C.; Baker, Jack W.; Luco, Nicolas

    2013-01-01

    The conditional spectrum (CS) is a target spectrum (with conditional mean and conditional standard deviation) that links seismic hazard information with ground-motion selection for nonlinear dynamic analysis. Probabilistic seismic hazard analysis (PSHA) estimates the ground-motion hazard by incorporating the aleatory uncertainties in all earthquake scenarios and resulting ground motions, as well as the epistemic uncertainties in ground-motion prediction models (GMPMs) and seismic source models. Typical CS calculations to date are produced for a single earthquake scenario using a single GMPM, but more precise use requires consideration of at least multiple causal earthquakes and multiple GMPMs that are often considered in a PSHA computation. This paper presents the mathematics underlying these more precise CS calculations. Despite requiring more effort to compute than approximate calculations using a single causal earthquake and GMPM, the proposed approach produces an exact output that has a theoretical basis. To demonstrate the results of this approach and compare the exact and approximate calculations, several example calculations are performed for real sites in the western United States. The results also provide some insights regarding the circumstances under which approximate results are likely to closely match more exact results. To facilitate these more precise calculations for real applications, the exact CS calculations can now be performed for real sites in the United States using new deaggregation features in the U.S. Geological Survey hazard mapping tools. Details regarding this implementation are discussed in this paper.

  13. A causal model for the effectiveness of internal quality assurance for the health science area.

    Science.gov (United States)

    Seeorn, Kittiya

    2005-10-01

    The purposes of this research were 1) to study the effectiveness of Internal Quality Assurance (IQA) of the Health science area, and 2) to study the factors affecting the effectiveness of the IQA of the Health science area. A causal model has been developed by the researcher comprised of the 6 exogenous latent variables: Attitude towards quality assurance, Teamwork, Staff training, Resource sufficiency, Organizational culture, and Leadership, and the 4 endogenous latent variables, which are the effectiveness of the IQA, Student-centered approach, Decentralized administration, PDCA cycle of work (Plan-Do-Check-Act), and Staff job satisfaction. The research sample consisted of 108 health science faculties derived by stratified random sampling technique. Data were collected by 10 questionnaires having reliability ranging from 0.79 to 0.96. Data analyses were descriptive statistics, and Linear Structure Relationship (LISREL) analysis. The major findings were as follows: 1. The 4 dimensions of effectiveness for the IQA of the Health science areas were significantly higher at the .05 level, after the Health science faculty applied the IQA programme according to the National Education Act of 1999. 2. The causal model of the effectiveness of the IQA was valid and fitted the empirical data. The 6 predictors accounted for 83% of the variance in the effectiveness of IQA. Culture and Leadership were the predictors that significantly accounted for the effectiveness of the IQA.

  14. Kramers-Kronig relations and causality conditions for graphene in the framework of the Dirac model

    Science.gov (United States)

    Klimchitskaya, G. L.; Mostepanenko, V. M.

    2018-04-01

    We analyze the concept of causality for the conductivity of graphene described by the Dirac model. It is recalled that the condition of causality leads to the analyticity of conductivity in the upper half-plane of complex frequencies and to the standard symmetry properties for its real and imaginary parts. This results in the Kramers-Kronig relations, which explicit form depends on whether the conductivity has no pole at zero frequency (as in the case of zero temperature when the band gap of graphene is larger than twice the chemical potential) or it has a pole (as in all other cases, specifically, at nonzero temperature). Through the direct analytic calculation it is shown that the real and imaginary parts of graphene conductivity, found recently on the basis of first principles of thermal quantum field theory using the polarization tensor in (2 +1 )-dimensional space-time, satisfy the Kramers-Kronig relations precisely. In so doing, the values of two integrals in the commonly used tables, which are also important for a wider area of dispersion relations in quantum field theory and elementary particle physics, are corrected. The obtained results are not of only fundamental theoretical character, but can be used as a guideline in testing the validity of different phenomenological approaches and for the interpretation of experimental data.

  15. Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI.

    Science.gov (United States)

    Bernal-Casas, David; Lee, Hyun Joo; Weitz, Andrew J; Lee, Jin Hyung

    2017-02-08

    Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell-type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific, brain-wide functional measurements-to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell-type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Dynamical dimensional reduction in toy models of 4D causal quantum gravity

    Science.gov (United States)

    Giasemidis, Georgios; Wheater, John F.; Zohren, Stefan

    2012-10-01

    In recent years several approaches to quantum gravity have found evidence for a scale dependent spectral dimension of space-time varying from four at large scales to two at small scales of order of the Planck length. The first evidence came from numerical results on four-dimensional causal dynamical triangulations (CDT) [Ambjørn , Phys. Rev. Lett. 95, 171301 (2005)]. Since then little progress has been made in analytically understanding the numerical results coming from the CDT approach and showing that they remain valid when taking the continuum limit. Here we argue that the spectral dimension can be determined from a model with fewer degrees of freedom obtained from the CDTs by radial reduction. In the resulting toy model we can take the continuum limit analytically and obtain a scale dependent spectral dimension varying from four to two with scale and having functional behavior exactly of the form which was conjectured on the basis of the numerical results.

  17. A Temporal-Causal Network Model for the Internal Processes of a Person with a Borderline Personality Disorder

    NARCIS (Netherlands)

    Hoțoiu, Maria; Tavella, Federico; Treur, Jan

    2018-01-01

    This paper presents a computational network model for a person with a Borderline Personality Disorder. It was designed according to a Network-Oriented Modeling approach as a temporal-causal network based on neuropsychological background knowledge. Some example simulations are discussed. The model

  18. Identifying abnormal connectivity in patients using Dynamic Causal Modelling of fMRI responses.

    Directory of Open Access Journals (Sweden)

    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.

  19. Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

    OpenAIRE

    Bollen, Kenneth A.; Bauldry, Shawn

    2011-01-01

    In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Caus...

  20. Comments on modeling the sound fields in an irregular ocean by causal first-order equations

    Science.gov (United States)

    Gulin, O. É.

    2008-05-01

    Two comments on using the causal matrix equations derived earlier for modeling the sound fields in a horizontally irregular medium are presented. The comments should be taken into account in practical calculations. The first of them is concerned with the discontinuities in the parameters of the medium along the propagation path. To eliminate the problems arising in this case, the mode evolution equations are modified to the case of matched boundaries of the irregular region and the layered part of the medium. The second comment refines the description of the specific case of a two-dimensionally inhomogeneous medium with the azimuthal symmetry in the horizontal plane. To reformulate the boundary-value problem to the problem for equations with initial conditions, the consideration of the more general three-dimensional initial problem with an azimuth angle is proposed with the sound source positioned at an arbitrary distance from the origin of coordinates.

  1. Causal universe

    CERN Document Server

    Ellis, George FR; Pabjan, Tadeusz

    2013-01-01

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

  2. Presenting the Students' Academic Achievement Causal Model based on Goal Orientation.

    Science.gov (United States)

    Nasiri, Ebrahim; Pour-Safar, Ali; Taheri, Mahdokht; Sedighi Pashaky, Abdullah; Asadi Louyeh, Ataollah

    2017-10-01

    Several factors play a role in academic achievement, individual's excellence and capability to do actions and tasks that the learner is in charge of in learning areas. The main goal of this study was to present academic achievement causal model based on the dimensions of goal orientation and learning approaches among the students of Medical Science and Dentistry courses in Guilan University of Medical Sciences in 2013. This study is based on a cross-sectional model. The participants included 175 first and second students of the Medical and Dentistry schools in Guilan University of Medical Sciences selected by random cluster sampling [121 persons (69%) Medical Basic Science students and 54 (30.9%) Dentistry students]. The measurement tool included the Goal Orientation Scale of Bouffard and Study Process Questionnaire of Biggs) and the students' Grade Point Average. The study data were analyzed using Pearson correlation coefficient and structural equations modeling. SPSS 14 and Amos were used to analyze the data. The results indicated a significant relationship between goal orientation and learning strategies (P<0.05). In addition, the results revealed that a significant relationship exists between learning strategies[Deep Learning (r=0.37, P<0.05), Surface Learning (r=-0.21,P<0.05)], and academic achievement.The suggested model of research is fitted to the data of the research. Results showed that the students' academic achievement model fits with experimental data, so it can be used in learning principles which lead to students' achievement in learning.

  3. Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

    Science.gov (United States)

    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

  4. Programs as Causal Models: Speculations on Mental Programs and Mental Representation

    Science.gov (United States)

    Chater, Nick; Oaksford, Mike

    2013-01-01

    Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of…

  5. Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform.

    Directory of Open Access Journals (Sweden)

    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.

  6. Individual decision-making in the causal pathway to addiction: contributions and limitations of rodent models.

    Science.gov (United States)

    Ahmed, Serge H

    2018-01-01

    The causal pathway from vulnerability to drug use and addiction involves a complex interaction between genetic, environmental, and behavioral factors. An individual can intervene on this causal pathway by two major types of individual decision. There is the inaugural, momentous decision to use a drug for the first time. This decision is influenced by both prior knowledge on the drug and its expected effects, and also by prior self-knowledge on one's own vulnerability. After an individual has used a drug for the first time, there is the decision to repeat drug use. This decision is influenced by the same factors that were involved in the inaugural decision to initiate drug use, except for one crucial difference. The first drug use has now acted on the individual, changing its brain acutely and also potentially persistently in a way that could bias subsequent decision-making in favor of repeated drug use. The goal of this review article is to assess the contributions and limitations of rodent models (i.e., rats, mice) to understand how prior drug use can influence decision-making in a way that favors future drug use. Overall, research on rodents shows that prior drug use can increase impulsive, risky and/or potentially harmful decision-making. However, this does not apparently translate into more drug use when rodents have the choice between a drug and a competing, nondrug option, except when the expected value of the latter is considerably decreased. The delayed drug reward hypothesis is developed to resolve and explain this apparent discrepancy. This novel hypothesis makes several unique predictions, some of them counterintuitive, and suggests that extrapolation of rodent research to humans should not only take into account differences in drug choice situations but also inherent species-specific differences in individual decision-making. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2018-03-01

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

  8. Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators.

    Science.gov (United States)

    Huang, Yen-Tsung; Pan, Wen-Chi

    2016-06-01

    Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high-dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the L2 norm of the component-wise effects, and develop a Monte-Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR-223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR-223 on GBM survival. © 2015, The International Biometric Society.

  9. Causal mapping

    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...

  10. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI☆

    Science.gov (United States)

    Koush, Yury; Rosa, Maria Joao; Robineau, Fabien; Heinen, Klaartje; W. Rieger, Sebastian; Weiskopf, Nikolaus; Vuilleumier, Patrik; Van De Ville, Dimitri; Scharnowski, Frank

    2013-01-01

    Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. PMID:23668967

  11. Dynamic causal modeling of touch-evoked potentials in the rubber hand illusion.

    Science.gov (United States)

    Zeller, Daniel; Friston, Karl J; Classen, Joseph

    2016-09-01

    The neural substrate of bodily ownership can be disclosed by the rubber hand illusion (RHI); namely, the illusory self-attribution of an artificial hand that is induced by synchronous tactile stimulation of the subject's hand that is hidden from view. Previous studies have pointed to the premotor cortex (PMC) as a pivotal area in such illusions. To investigate the effective connectivity between - and within - sensory and premotor areas involved in bodily perceptions, we used dynamic causal modeling of touch-evoked responses in 13 healthy subjects. Each subject's right hand was stroked while viewing their own hand ("REAL"), or an artificial hand presented in an anatomically plausible ("CONGRUENT") or implausible ("INCONGRUENT") position. Bayesian model comparison revealed strong evidence for a differential involvement of the PMC in the generation of touch-evoked responses under the three conditions, confirming a crucial role of PMC in bodily self-attribution. In brief, the extrinsic (forward) connection from left occipital cortex to left PMC was stronger for CONGRUENT and INCONGRUENT as compared to REAL, reflecting the augmentation of bottom-up visual input when multisensory integration is challenged. Crucially, intrinsic connectivity in the primary somatosensory cortex (S1) was attenuated in the CONGRUENT condition, during the illusory percept. These findings support predictive coding models of the functional architecture of multisensory integration (and attenuation) in bodily perceptual experience. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

    Science.gov (United States)

    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.…

  13. Peer Cluster Theory and Adolescent Alcohol Use: An Explanation of Alcohol Use and Comparative Analysis between Two Causal Models.

    Science.gov (United States)

    Rose, Christopher D.

    1999-01-01

    Tests the premise of peer cluster theory as it applies to individual alcohol use, and makes a comparative analysis between its ability to explain alcohol use and marijuana use among college students (N=1312). Results of the causal models show some support for peer cluster theory. Discusses the study's limitations and implications. (Author/MKA)

  14. Comparison of two integration methods for dynamic causal modeling of electrophysiological data.

    Science.gov (United States)

    Lemaréchal, Jean-Didier; George, Nathalie; David, Olivier

    2018-06-01

    Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system. In this technical note, we compared two numerical schemes for the integration of DDEs. The first, and standard, scheme approximates the DDEs (more precisely, the state of the system, with respect to conduction delays among brain regions) using ordinary differential equations (ODEs) and solves it with a fixed step size. The second scheme uses a dedicated DDEs solver with adaptive step sizes to control error, making it theoretically more accurate. To highlight the effects of the approximation used by the first integration scheme in regard to parameter estimation and Bayesian model selection, we performed simulations of local field potentials using first, a simple model comprising 2 regions and second, a more complex model comprising 6 regions. In these simulations, the second integration scheme served as the standard to which the first one was compared. Then, the performances of the two integration schemes were directly compared by fitting a public mismatch negativity EEG dataset with different models. The simulations revealed that the use of the standard DCM integration scheme was acceptable for Bayesian model selection but underestimated the connectivity parameters and did not allow an accurate estimation of conduction delays. Fitting to empirical data showed that the models systematically obtained an increased accuracy when using the second

  15. 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.

  16. Presenting the students’ academic achievement causal model based on goal orientation

    Directory of Open Access Journals (Sweden)

    EBRAHIM NASIRI

    2017-10-01

    Full Text Available Introduction: Several factors play a role in academic achievement, individual’s excellence and capability to do actions and tasks that the learner is in charge of in learning areas. The main goal of this study was to present academic achievement causal model based on the dimensions of goal orientation and learning approaches among the students of Medical Science and Dentistry courses in Guilan University of Medical Sciences in 2013. Methods: This study is based on a cross-sectional model. The participants included 175 first and second year students of the Medical and Dentistry schools in Guilan University of Medical Sciences selected by random cluster sampling [121 persons (69% Medical Basic Science students and 54 (30.9% Dentistry students]. The measurement tool included the Goal Orientation Scale of Bouffard and Study Process Questionnaire of Biggs and the students’ Grade Point Average. The study data were analyzed using Pearson correlation coefficient and structural equations modeling. SPSS 14 and Amos were used to analyze the data. Results: The results indicated a significant relationship between goal orientation and learning strategies (P<0.05. In addition, the results revealed that a significant relationship exists between learning strategies [Deep Learning (r=0.37, P<0.05, Surface Learning (r=-0.21, P<0.05], and academic achievement. The suggested model of research is fitted to the data of the research. Conclusion: Results showed that the students’ academic achievement model fits with experimental data, so it can be used in learning principles which lead to students’ achievement in learning.

  17. Causality in demand

    DEFF Research Database (Denmark)

    Nielsen, Max; Jensen, Frank; Setälä, Jari

    2011-01-01

    to fish demand. On the German market for farmed trout and substitutes, it is found that supply sources, i.e. aquaculture and fishery, are not the only determinant of causality. Storing, tightness of management and aggregation level of integrated markets might also be important. The methodological......This article focuses on causality in demand. A methodology where causality is imposed and tested within an empirical co-integrated demand model, not prespecified, is suggested. The methodology allows different causality of different products within the same demand system. The methodology is applied...... implication is that more explicit focus on causality in demand analyses provides improved information. The results suggest that frozen trout forms part of a large European whitefish market, where prices of fresh trout are formed on a relatively separate market. Redfish is a substitute on both markets...

  18. Non-Causal Computation

    Directory of Open Access Journals (Sweden)

    Ämin Baumeler

    2017-07-01

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

  19. 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.

  20. Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-01-01

    Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with

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

    Science.gov (United States)

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

    2010-09-01

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

  2. Causal modeling of self-concept, job satisfaction, and retention of nurses.

    Science.gov (United States)

    Cowin, Leanne S; Johnson, Maree; Craven, Rhonda G; Marsh, Herbert W

    2008-10-01

    The critical shortage of nurses experienced throughout the western world has prompted researchers to examine one major component of this complex problem - the impact of nurses' professional identity and job satisfaction on retention. A descriptive correlational design with a longitudinal element was used to examine a causal model of nurses' self-concept, job satisfaction, and retention plans in 2002. A random sample of 2000 registered nurses was selected from the state registering authority listing. A postal survey assessing multiple dimensions of nurses' self-concept (measured by the nurse self-concept questionnaire), job satisfaction (measured by the index of work satisfaction) was undertaken at Time 1 (n=528) and 8 months later at Time 2 (n=332) (including retention plans (measured by the Nurse Retention Index). Using confirmatory factor analysis, correlation matrices and path analysis, measurement and structural models were examined on matching pairs of data from T1 and T2 (total sample N=332). Nurses' self-concept was found to have a stronger association with nurses' retention plans (B=.45) than job satisfaction (B=.28). Aspects of pay and task were not significantly related to retention plans, however, professional status (r=.51), and to a lesser extent, organizational policies (r=.27) were significant factors. Nurses' general self-concept was strongly related (r=.57) to retention plans. Strategies or interventions requiring implementation and evaluation include: counseling to improve nurse general self-concept, education programs and competencies in health communication between health professionals, reporting of nurse-initiated programs with substantial patient benefit, nurse-friendly organizational policies, common health team learning opportunities, and autonomous practice models.

  3. Causality and headache triggers

    Science.gov (United States)

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

    2013-01-01

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

  4. Epidemiological causality.

    Science.gov (United States)

    Morabia, Alfredo

    2005-01-01

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

  5. The Teacher, the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs

    Science.gov (United States)

    Boerebach, Benjamin C. M.; Lombarts, Kiki M. J. M. H.; Scherpbier, Albert J. J.; Arah, Onyebuchi A.

    2013-01-01

    Background In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty’s teaching performance on their role modeling. Therefore, there is a need for robust frameworks and methods for transparent formal presentation of the underlying causal assumptions used in assessing the causal effects of teaching performance on role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes. Methods This study revisits a previously published study about the influence of faculty’s teaching performance on their role modeling (as teacher-supervisor, physician and person). We drew eight directed acyclic graphs (DAGs) to visually represent different plausible causal relationships between the variables under study. These DAGs were subsequently translated into corresponding statistical models, and regression analyses were performed to estimate the associations between teaching performance and role modeling. Results The different causal models were compatible with major differences in the magnitude of the relationship between faculty’s teaching performance and their role modeling. Odds ratios for the associations between teaching performance and the three role model types ranged from 31.1 to 73.6 for the teacher-supervisor role, from 3.7 to 15.5 for the physician role, and from 2.8 to 13.8 for the person role. Conclusions Different sets of assumptions about causal relationships in role modeling research can be visually depicted using DAGs, which are then used to guide both statistical analysis and interpretation of results. Since study conclusions can be sensitive to different causal assumptions, results

  6. Altered retrieval of melodic information in congenital amusia: insights from dynamic causal modeling of MEG data.

    Science.gov (United States)

    Albouy, Philippe; Mattout, Jérémie; Sanchez, Gaëtan; Tillmann, Barbara; Caclin, Anne

    2015-01-01

    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 short-term 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 short-term memory 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 notably an increase in "Same" 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.

  7. Altered retrieval of melodic information in congenital amusia: Insights from Dynamic Causal Modeling of MEG data

    Directory of Open Access Journals (Sweden)

    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.

  8. Dynamic panel data models and causality : Applications to labor supply, health and insurance

    NARCIS (Netherlands)

    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,

  9. Calculating and Understanding: Formal Models and Causal Explanations in Science, Common Reasoning and Physics Teaching

    Science.gov (United States)

    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…

  10. A Causal Model of Career Development and Quality of Life of College Students with Disabilities

    Science.gov (United States)

    Chun, Jina

    2017-01-01

    Researchers have assumed that social cognitive factors play significant roles in the career development of transition youth and young adults with disabilities and those without disabilities. However, research on the influence of the career decision-making process as a primary causal agent in one's psychosocial outcomes such as perceived level of…

  11. Causal inference in econometrics

    CERN Document Server

    Kreinovich, Vladik; Sriboonchitta, Songsak

    2016-01-01

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

  12. Integrative analysis of functional genomic annotations and sequencing data to identify rare causal variants via hierarchical modeling

    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

  13. Using causal models to distinguish between neurogenesis-dependent and -independent effects on behaviour

    OpenAIRE

    Lazic, Stanley E.

    2011-01-01

    There has been a substantial amount of research on the relationship between hippocampal neurogenesis and behaviour over the past fifteen years, but the causal role that new neurons have on cognitive and affective behavioural tasks is still far from clear. This is partly due to the difficulty of manipulating levels of neurogenesis without inducing off-target effects, which might also influence behaviour. In addition, the analytical methods typically used do not directly test whether neurogenes...

  14. An environmental impact causal model for improving the environmental performance of construction processes

    OpenAIRE

    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...

  15. Causal compositional models in valuation-based systems with examples in specific theories

    Czech Academy of Sciences Publication Activity Database

    Jiroušek, Radim; Shenoy, P. P.

    2016-01-01

    Roč. 72, č. 1 (2016), s. 95-112 ISSN 0888-613X Grant - others:GA ČR(CZ) GA15-00215S Institutional support: RVO:67985556 Keywords : operator of composition * causality * belief function Subject RIV: AH - Economics OBOR OECD: Economic Theory Impact factor: 2.845, year: 2016 http://library.utia.cas.cz/separaty/2017/MTR/jirousek-0481260.pdf

  16. Deconstructing Constructivism: Modeling Causal Relationships Among Constructivist Learning Environment Factors and Student Outcomes in Introductory Chemistry

    Science.gov (United States)

    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

  17. Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

    Directory of Open Access Journals (Sweden)

    Hannah H Leslie

    Full Text Available OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC use and cervical intraepithelial neoplasia 2 or greater (CIN2+ and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7% were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9% increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an

  18. A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in MATLAB.

    Science.gov (United States)

    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.

  19. Medical Disease or Moral Defect? Stigma Attribution and Cultural Models of Addiction Causality in a University Population.

    Science.gov (United States)

    Henderson, Nicole L; Dressler, William W

    2017-12-01

    This study examines the knowledge individuals use to make judgments about persons with substance use disorder. First, we show that there is a cultural model of addiction causality that is both shared and contested. Second, we examine how individuals' understanding of that model is associated with stigma attribution. Research was conducted among undergraduate students at the University of Alabama. College students in the 18-25 age range are especially at risk for developing substance use disorder, and they are, perhaps more than any other population group, intensely targeted by drug education. The elicited cultural model includes different types of causes distributed across five distinct themes: Biological, Self-Medication, Familial, Social, and Hedonistic. Though there was cultural consensus among respondents overall, residual agreement analysis showed that the cultural model of addiction causality is a multicentric domain. Two centers of the model, the moral and the medical, were discovered. Differing adherence to these centers is associated with the level of stigma attributed towards individuals with substance use disorder. The results suggest that current approaches to substance use education could contribute to stigma attribution, which may or may not be inadvertent. The significance of these results for both theory and the treatment of addiction are discussed.

  20. Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging.

    Science.gov (United States)

    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. © 2013.

  1. Neuromuscular Alterations After Ankle Sprains: An Animal Model to Establish Causal Links After Injury.

    Science.gov (United States)

    Lepley, Lindsey K; McKeon, Patrick O; Fitzpatrick, Shane G; Beckemeyer, Catherine L; Uhl, Timothy L; Butterfield, Timothy A

    2016-10-01

    The mechanisms that contribute to the development of chronic ankle instability are not understood. Investigators have developed a hypothetical model in which neuromuscular alterations that stem from damaged ankle ligaments are thought to affect periarticular and proximal muscle activity. However, the retrospective nature of these studies does not allow a causal link to be established. To assess temporal alterations in the activity of 2 periarticular muscles of the rat ankle and 2 proximal muscles of the rat hind limb after an ankle sprain. Controlled laboratory study. Laboratory. Five healthy adult male Long Evans rats (age = 16 weeks, mass = 400.0 ± 13.5 g). Indwelling fine-wire electromyography (EMG) electrodes were implanted surgically into the biceps femoris, medial gastrocnemius, vastus lateralis, and tibialis anterior muscles of the rats. We recorded baseline EMG measurements while the rats walked on a motor-driven treadmill and then induced a closed lateral ankle sprain by overextending the lateral ankle ligaments. After ankle sprain, the rats were placed on the treadmill every 24 hours for 7 days, and we recorded postsprain EMG data. Onset time of muscle activity, phase duration, sample entropy, and minimal detectable change (MDC) were assessed and compared with baseline using 2-tailed dependent t tests. Compared with baseline, delayed onset time of muscle activity was exhibited in the biceps femoris (baseline = -16.7 ± 54.0 milliseconds [ms]) on day 0 (5.2 ± 64.1 ms; t 4 = -4.655, P = .043) and tibialis anterior (baseline = 307.0 ± 64.2 ms) muscles on day 3 (362.5 ± 55.9 ms; t 4 = -5.427, P = .03) and day 6 (357.3 ± 39.6 ms; t 4 = -3.802, P = .02). Longer phase durations were observed for the vastus lateralis (baseline = 321.9 ± 92.6 ms) on day 3 (401.3 ± 101.2 ms; t 3 = -4.001, P = .03), day 4 (404.1 ± 93.0 ms; t 3 = -3.320, P = .048), and day 5 (364.6 ± 105.2 ms; t 3 = -3.963, P = .03) and for the tibialis anterior (baseline = 103.9 ± 16.4 ms

  2. Path integrals on causal sets

    International Nuclear Information System (INIS)

    Johnston, Steven

    2009-01-01

    We describe a quantum mechanical model for particle propagation on a causal set. The model involves calculating a particle propagator by summing amplitudes assigned to trajectories within the causal set. This 'discrete path integral' is calculated using a matrix geometric series. Amplitudes are given which, when the causal set is generated by sprinkling points into 1+1 or 3+1 Minkowski spacetime, ensure the particle propagator agrees in a suitable sense, with the retarded causal propagator for the Klein-Gordon equation.

  3. 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.

  4. Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections.

    Science.gov (United States)

    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

  5. Dynamic causal modeling of hippocampal links within the human default mode network: Lateralization and computational stability of effective connections

    Directory of Open Access Journals (Sweden)

    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

  6. Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections

    Science.gov (United States)

    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

  7. Covariation in Natural Causal Induction.

    Science.gov (United States)

    Cheng, Patricia W.; Novick, Laura R.

    1991-01-01

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

  8. 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 = = for a transition amplitude between a preparation lt. slashA> and a measurement lt. slashB>, 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 the authors retrocausation concept but the hidden-variables assumption he has unwittingly made.

  9. On the zigzagging causality EPR model: Answer to Vigier and coworkers and to Sutherland

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Ibrahim Delibalta

    2017-01-01

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

  11. 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

    Designing complex heterogeneousmultiprocessor Systemon- Chip (MPSoC) requires support for modeling and analysis of the different layers i.e. application, operating system (OS) and platform architecture. This paper presents an abstract system-level modeling framework, called ARTS, to support...... 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...... on processor, memory and communication performance. In particular, we focus on analyzing the impact of application mapping on the processor and memory utilization taking the on-chip communication latency into account. A case-study of real-time multimedia application consisting of 114 tasks on a 6-processor...

  12. Causal model of safety-checking action of the staff of nuclear power plants and the organization climate

    International Nuclear Information System (INIS)

    Fukui, Hirokazu; Yoshida, Michio; Yamaura, Kazuho

    2000-01-01

    For those who run an organization, it is critical to identify the causal relationship between the organization's characteristics and the safety-checking action of its staff, in order to effectively implement activities for promoting safety. In this research. a causal model of the safety-checking action was developed and factors affecting it were studied. A questionnaire survey, which includes safety awareness, attitude toward safety, safety culture and others, was conducted at three nuclear power plants and eight factors were extracted by means of factor analysis of the questionnaire items. The extracted eight interrelated factors were as follows: work norm, supervisory action, interest in training, recognition of importance, safety-checking action, the subject of safety, knowledge/skills, and the attitude of an organization. Among them, seven factors except the recognition of importance were defined as latent variables and a causal model of safety-checking action was constructed. By means of covariance structure analysis, it was found that the three factors: the attitude of an organization, supervisory action and the subject of safety, have a significant effect on the safety-checking action. Moreover, it was also studied that workplaces in which these three factors are highly regarded form social environment where safety-checking action is fully supported by the workplace as a whole, while workplaces in which these three factors are poorly regarded do not fully form social environment where safety-checking action is supported. Therefore, the workplaces form an organizational environment where safety-checking action tends to depend strongly upon the knowledge or skills of individuals. On top of these, it was noted that the attitude of an organization and supervisory action are important factors that serve as the first trigger affecting the formation of the organizational climate for safety. (author)

  13. Using simple causal modeling to understand how water and temperature affect daily stem radial variation in trees.

    Science.gov (United States)

    Deslauriers, Annie; Anfodillo, Tommaso; Rossi, Sergio; Carraro, Vinicio

    2007-08-01

    Variation in tree stem diameter results from reversible shrinking and swelling and irreversible radial growth, all processes that are influenced by tree water status. To assess the causal effects of water and temperature on stem radial variation (DeltaR) and maximum daily shrinkage (MDS), the diurnal cycle was divided into three phases: contraction, expansion and stem radius increment. Diurnal cycles were measured during 1996-2004 in Picea abies (L.) Karst., Pinus cembra L. and Larix decidua Mill. in a timberline ecotone to understand the links between stem diameter variation (v; defined as MDS or DR), phase duration (h), and weather or sap flow descriptors (d). We demonstrated that a high proportion of MDS and DeltaR was explained by h because of the nonlinearity of the physiological responses to weather d. By causal modeling, we tested whether the relationship between d and v was due to h (lack of causal relationship between d and v) or to both d and h (double cause). The results of this modeling added new physiological insight into daily growth-climate relationships. Negative correlations were found between DeltaR and air temperature owing to the negative effect of temperature on h only, and did not correspond to a direct effect on tree growth mediated by an alteration in metabolic activities. Precipitation had two main effects: a direct effect on DeltaR and an indirect effect mediated through an effect on h. A reduction in sap flow at night led to an increase in DeltaR for P. abies and L. decidua, but not for P. cembra.

  14. Dynamics Of Causal Sets

    CERN Document Server

    Rideout, D P

    2001-01-01

    The Causal Set approach to quantum gravity asserts that spacetime, at its smallest length scale, has a discrete structure. This discrete structure takes the form of a locally finite order relation, where the order, corresponding with the macroscopic notion of spacetime causality, is taken to be a fundamental aspect of nature. After an introduction to the Causal Set approach, this thesis considers a simple toy dynamics for causal sets. Numerical simulations of the model provide evidence for the existence of a continuum limit. While studying this toy dynamics, a picture arises of how the dynamics can be generalized in such a way that the theory could hope to produce more physically realistic causal sets. By thinking in terms of a stochastic growth process, and positing some fundamental principles, we are led almost uniquely to a family of dynamical laws (stochastic processes) parameterized by a countable sequence of coupling constants. This result is quite promising in that we now know how to speak of dynamics ...

  15. Causality Statistical Perspectives and Applications

    CERN Document Server

    Berzuini, Carlo; Bernardinell, Luisa

    2012-01-01

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

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

    Science.gov (United States)

    Markus, Keith A.

    2010-01-01

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

  17. The Teacher, the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs

    OpenAIRE

    Boerebach, Benjamin C. M.; Lombarts, Kiki M. J. M. H.; Scherpbier, Albert J. J.; Arah, Onyebuchi A.

    2013-01-01

    BACKGROUND: In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty's teaching performance on their role modeling. Therefore, there is a need for robust frame...

  18. Explaining through causal mechanisms

    NARCIS (Netherlands)

    Biesbroek, Robbert; Dupuis, Johann; Wellstead, Adam

    2017-01-01

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

  19. Molecular epidemiology of acute leukemia in children: causal model, interaction of three factors-susceptibility, environmental exposure and vulnerability period.

    Science.gov (United States)

    Mejía-Aranguré, Juan Manuel

    Acute leukemias have a huge morphological, cytogenetic and molecular heterogeneity and genetic polymorphisms associated with susceptibility. Every leukemia presents causal factors associated with the development of the disease. Particularly, when three factors are present, they result in the development of acute leukemia. These phenomena are susceptibility, environmental exposure and a period that, for this model, has been called the period of vulnerability. This framework shows how the concepts of molecular epidemiology have established a reference from which it is more feasible to identify the environmental factors associated with the development of leukemia in children. Subsequently, the arguments show that only susceptible children are likely to develop leukemia once exposed to an environmental factor. For additional exposure, if the child is not susceptible to leukemia, the disease does not develop. In addition, this exposure should occur during a time window when hematopoietic cells and their environment are more vulnerable to such interaction, causing the development of leukemia. This model seeks to predict the time when the leukemia develops and attempts to give a context in which the causality of childhood leukemia should be studied. This information can influence and reduce the risk of a child developing leukemia. Copyright © 2016 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.

  20. 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...

  1. Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference

    Science.gov (United States)

    Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan

    2015-01-01

    This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

  2. Paradoxical Behavior of Granger Causality

    Science.gov (United States)

    Witt, Annette; Battaglia, Demian; Gail, Alexander

    2013-03-01

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

  3. Estimation of Causal Mediation Effects for a Dichotomous Outcome in Multiple-Mediator Models using the Mediation Formula

    Science.gov (United States)

    Nelson, Suchitra; Albert, Jeffrey M.

    2013-01-01

    Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a non-zero total mediation effect increases as the correlation coefficient between two mediators increases, while power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. PMID:23650048

  4. A theory of causal learning in children: causal maps and Bayes nets.

    Science.gov (United States)

    Gopnik, Alison; Glymour, Clark; Sobel, David M; Schulz, Laura E; Kushnir, Tamar; Danks, David

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

  5. A theory of causal learning in children: Causal maps and Bayes nets

    OpenAIRE

    Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D

    2004-01-01

    The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...

  6. Depression alters "top-down" visual attention: a dynamic causal modeling comparison between depressed and healthy subjects.

    Science.gov (United States)

    Desseilles, Martin; Schwartz, Sophie; Dang-Vu, Thien Thanh; Sterpenich, Virginie; Ansseau, Marc; Maquet, Pierre; Phillips, Christophe

    2011-01-15

    Using functional magnetic resonance imaging (fMRI), we recently demonstrated that nonmedicated patients with a first episode of unipolar major depression (MDD) compared to matched controls exhibited an abnormal neural filtering of irrelevant visual information (Desseilles et al., 2009). During scanning, subjects performed a visual attention task imposing two different levels of attentional load at fixation (low or high), while task-irrelevant colored stimuli were presented in the periphery. In the present study, we focused on the visuo-attentional system and used "Dynamic Causal Modeling" (DCM) on the same dataset to assess how attention influences a network of three dynamically-interconnected brain regions (visual areas V1 and V4, and intraparietal sulcus (P), differentially in MDD patients and healthy controls. Bayesian model selection (BMS) and model space partitioning (MSP) were used to determine the best model in each population. The best model for the controls revealed that the increase of parietal activity by high attention load was selectively associated with a negative modulation of P on V4, consistent with high attention reducing the processing of irrelevant colored peripheral stimuli. The best model accounting for the data from the MDD patients showed that both low and high attention levels exerted modulatory effects on P. The present results document abnormal effective connectivity across visuo-attentional networks in MDD, which likely contributes to deficient attentional filtering of information. Copyright © 2010 Elsevier Inc. All rights reserved.

  7. Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

    Science.gov (United States)

    Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

    2017-07-01

    The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

  8. Applying a Multiple Group Causal Indicator Modeling Framework to the Reading Comprehension Skills of Third, Seventh, and Tenth Grade Students

    Science.gov (United States)

    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 level. This model included latent predictor constructs of decoding, verbal reasoning, nonverbal reasoning, and working memory and accounted for a large portion of the reading comprehension variance (73% to 87%) across grade levels. Verbal reasoning contributed the most unique variance to reading comprehension at all grade levels. In addition, we fit a multiple group 4-factor MIMIC model to investigate the relative stability (or variability) of the predictor contributions to reading comprehension across development (i.e., grade levels). The results revealed that the contributions of verbal reasoning, nonverbal reasoning, and working memory to reading comprehension were stable across the three grade levels. Decoding was the only predictor that could not be constrained to be equal across grade levels. The contribution of decoding skills to reading comprehension was higher in third grade and then remained relatively stable between seventh and tenth grade. These findings illustrate the feasibility of using MIMIC models to explain individual differences in reading comprehension across the development of reading skills. PMID:25821346

  9. Behavioural Pattern of Causality Parameter of Autoregressive ...

    African Journals Online (AJOL)

    In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...

  10. System-level causal modelling of widescale resource plundering: Acting on the rhino poaching catastrophe

    CSIR Research Space (South Africa)

    Koen, Hildegarde S

    2017-10-01

    Full Text Available The initial goal of this study was to develop a predictive model that could serve as a pre-emptive method for curbing rhino poaching. During the development of the predictive model it became evident that only the tip of the iceberg, so to speak, has...

  11. Perceived Difficulty of Moral Dilemmas Depends on Their Causal Structure: A Formal Model and Preliminary Results

    DEFF Research Database (Denmark)

    Kuhnert, Barbara; Lindner, Felix; Bentzen, Martin Mose

    models, we cluster a set of dilemmas in Type 1 and Type 2 dilemmas. We observe that for Type 2 dilemmas but not for Type 1 dilemmas a utilitarian action dominates the possibility of refraining from action. Hence, we hypothesize, based on the model, that Type 2 dilemmas are perceived as less difficult...

  12. The Role of Student Involvement and Perceptions of Integration in a Causal Model of Student Persistence.

    Science.gov (United States)

    Berger, Joseph B.; Milem, Jeffrey F.

    1999-01-01

    This study refined and applied an integrated model of undergraduate persistence (accounting for both behavioral and perceptual components) to examine first-year retention at a private, highly selective research university. Results suggest that including behaviorally based measures of involvement improves the model's explanatory power concerning…

  13. 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...

  14. [Path causal analysis of a model of a functional organization between defense mechanisms and coping strategies].

    Science.gov (United States)

    Gouvernet, B; Mouchard, J; Combaluzier, S

    2015-10-01

    In the psychological literature, two concepts are often used to approach psychological and social adaptation: defense mechanisms and coping strategies. Many empirical studies deal with these strategies independently of each other. However, the nature of their relationship is still debated, making empirical studies necessary jointly evaluating these two types of strategies to better reflect the adaptive process. To test Chabrol and Callahan's theoretical model of the relationship between defence mechanisms and coping strategies. According to theses authors, defence mechanisms and coping strategies are distinct mechanisms, functionally organized: defenses appear first and modulate the emergence of coping strategy defenses through threat representation. Ninety-four young adult volunteers completed the Coping Inventory for Stressful Situations (CISS), the Defense Style Questionnaire (DSQ-40) and the Perceived Stress Scale (PSS14). The data were treated according to the structural equation modeling method. Overall, the results support the theoretical model proposed by Chabrol and Callahan. The statistical model provides a good fit to the data (chi(2)/df=18.62/22=.85, P=.67, RMSEA=.00 (90% CI: .00-.07), CFI=1, TLI=1.04). It explains from 7 to 24% of coping variability scores (Avoidant Coping: R(2)=.07, Pcritical in stress management. Copyright © 2014 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

  15. A causal model of depression among older adults in Chon Buri Province, Thailand.

    Science.gov (United States)

    Piboon, Kanchana; Subgranon, Rarcharneeporn; Hengudomsub, Pornpat; Wongnam, Pairatana; Louise Callen, Bonnie

    2012-02-01

    The purposes of this study are to develop and empirically test a theoretical model that examines the relationships between a set of predictors and depression among older adults. A biopsychosocial model was tested with 317 community dwelling older adults residing in Chon Buri Province, Thailand. A face-to-face interview was used in a cross-sectional community-based survey. A hypothesized model of depression was tested by using path analysis. It was found that the modified model fitted the data and the predictors accounted for 60% of the variance in depression. Female gender, activities of daily living, loneliness, stressful life events, and emotional-focused coping had a positive direct effect on depression. Social support and problem-focused coping had a negative direct effect on depression. Additionally, perceived stress, stressful life events, loneliness, and income had a negative indirect effect on depression through social support. Female gender, activities of daily living, and perceived stress also had a positive indirect effect on depression through emotional-focused coping. Stressful life events, perceived stress, and income had a negative indirect effect on depression through problem-focused coping. These findings contribute to a better understanding of the variables that predict depression in older adults. Thus, health care providers should consider the effects of these contributing factors on depression in the older adult person and can devise a program to prevent and promote health in older adults alleviating depression.

  16. Neural networks for action representation underlying automatic mimicry: A functional magnetic-resonance imaging and dynamic causal modeling study

    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.

  17. Academic self-concept, interest, grades, and standardized test scores: reciprocal effects models of causal ordering.

    Science.gov (United States)

    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.

  18. Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations.

    Science.gov (United States)

    Gilbert, Jessica R; Symmonds, Mkael; Hanna, Michael G; Dolan, Raymond J; Friston, Karl J; Moran, Rosalyn J

    2016-01-01

    Clinical assessments of brain function rely upon visual inspection of electroencephalographic waveform abnormalities in tandem with functional magnetic resonance imaging. However, no current technology proffers in vivo assessments of activity at synapses, receptors and ion-channels, the basis of neuronal communication. Using dynamic causal modeling we compared electrophysiological responses from two patients with distinct monogenic ion channelopathies and a large cohort of healthy controls to demonstrate the feasibility of assaying synaptic-level channel communication non-invasively. Synaptic channel abnormality was identified in both patients (100% sensitivity) with assay specificity above 89%, furnishing estimates of neurotransmitter and voltage-gated ion throughput of sodium, calcium, chloride and potassium. This performance indicates a potential novel application as an adjunct for clinical assessments in neurological and psychiatric settings. More broadly, these findings indicate that biophysical models of synaptic channels can be estimated non-invasively, having important implications for advancing human neuroimaging to the level of non-invasive ion channel assays. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Arup Kumar Baksi

    2012-08-01

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

  20. From meta-omics to causality: experimental models for human microbiome research.

    Science.gov (United States)

    Fritz, Joëlle V; Desai, Mahesh S; Shah, Pranjul; Schneider, Jochen G; Wilmes, Paul

    2013-05-03

    Large-scale 'meta-omic' projects are greatly advancing our knowledge of the human microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case-control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation.

  1. Towards the Accuracy of Cybernetic Strategy Planning Models: Causal Proof and Function Approximation

    Directory of Open Access Journals (Sweden)

    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.

  2. α-Decomposition for estimating parameters in common cause failure modeling based on causal inference

    International Nuclear Information System (INIS)

    Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi

    2013-01-01

    The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system

  3. Closures and mergers of VNA home care agencies: a model for the study of causal factors.

    Science.gov (United States)

    Scalzi, C C; Meyer, M

    1991-01-01

    Recent closures and mergers of visiting nurses associations (VNAs) raise some potentially serious questions regarding access to home care for the elderly Medicare and Medicaid populations. While VNAs comprise less than 10 percent of the nation's certified home health agencies, they provide approximately 31 percent of all medicare home care (HCFA, 1989). In March of 1986 there were 524 visiting nurses associations nationally whereas today there remain only 495 (HCFA, 1990). A model identifying potential causes of VNA mortality is presented along with some preliminary results. VNA mortality is defined and measured by the date that Medicare certification is terminated as a result of VNA closure or merger.

  4. Radon levels in dwellings in chalk terrain. Development and analysis of distributional and causal models

    International Nuclear Information System (INIS)

    Killip, Ian Richmond

    2002-01-01

    This thesis investigates the range, distribution and causes of high radon levels in dwellings in the Brighton area of Southeast England. Indoor radon levels were measured in more than 1000 homes. The results show that high radon levels can arise in an area previously considered to offer low radon potential from local geological sources. Climate and building-related factors were found to affect significantly the radon levels in dwellings. Multiple regression was used to determine the influence of the various factors on indoor radon levels and an empirical model develop to predict indoor radon levels. The radon hazard, independent of building-related effects, was determined for each surveyed location by adjusting the radon measurement to that expected on the ground floor of a 'model' dwelling. This standardised set of radon levels was entered into a geographical information system (GIS) and related to surface geology. The geometric mean radon level for each lithological unit was plotted to produce a radon hazard map for the area. The highest radon levels were found to be associated with the youngest Chalk Formations, particularly where they meet overlying Tertiary deposits, and with Clay-with-Flints Quaternary deposits in the area. The results were also converted to the radon activity equivalent to that expected from the NRPB's standard dual-detector dwelling survey method and analysed by lognormal modelling to estimate the proportion of dwellings likely to exceed the UK Action Level of 200 Bq/m 3 for each lithological unit. The likely percentages of dwellings affected by radon thus obtained were mapped to lithological boundaries to produce a radon potential map. The radon hazard map and the empirical radon model facilitate the prediction of radon levels in dwellings of comparable construction and above similar geology and should further the understanding of the behaviour of radon gas in buildings to allow indoor radon concentrations to be controlled. The radon

  5. Causal Mediation Analysis for the Cox Proportional Hazards Model with a Smooth Baseline Hazard Estimator.

    Science.gov (United States)

    Wang, Wei; Albert, Jeffrey M

    2017-08-01

    An important problem within the social, behavioral, and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model in this paper. We give precise definitions of the total effect, natural indirect effect, and natural direct effect in terms of the survival probability, hazard function, and restricted mean survival time within the standard two-stage mediation framework. To estimate the mediation effects on different scales, we propose a mediation formula approach in which simple parametric models (fractional polynomials or restricted cubic splines) are utilized to approximate the baseline log cumulative hazard function. Simulation study results demonstrate low bias of the mediation effect estimators and close-to-nominal coverage probability of the confidence intervals for a wide range of complex hazard shapes. We apply this method to the Jackson Heart Study data and conduct sensitivity analysis to assess the impact on the mediation effects inference when the no unmeasured mediator-outcome confounding assumption is violated.

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

    CERN Document Server

    Dribus, Benjamin F

    2017-01-01

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

  7. Altered connectivity pattern of hubs in default-mode network with Alzheimer's disease: an Granger causality modeling approach.

    Science.gov (United States)

    Miao, Xiaoyan; Wu, Xia; Li, Rui; Chen, Kewei; Yao, Li

    2011-01-01

    Evidences from normal subjects suggest that the default-mode network (DMN) has posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) as its hubs; meanwhile, these DMN nodes are often found to be abnormally recruited in Alzheimer's disease (AD) patients. The issues on how these hubs interact to each other, with the rest nodes of the DMN and the altered pattern of hubs with respect to AD, are still on going discussion for eventual final clarification. To address these issues, we investigated the causal influences between any pair of nodes within the DMN using Granger causality analysis and graph-theoretic methods on resting-state fMRI data of 12 young subjects, 16 old normal controls and 15 AD patients respectively. We found that: (1) PCC/MPFC/IPC, especially the PCC, showed the widest and distinctive causal effects on the DMN dynamics in young group; (2) the pattern of DMN hubs was abnormal in AD patients compared to old control: MPFC and IPC had obvious causal interaction disruption with other nodes; the PCC showed outstanding performance for it was the only region having causal relation with all other nodes significantly; (3) the altered relation between hubs and other DMN nodes held potential as a noninvasive biomarker of AD. Our study, to the best of our knowledge, is the first to support the hub configuration of the DMN from the perspective of causal relationship, and reveal abnormal pattern of the DMN hubs in AD. Findings from young subjects provide additional evidence for the role of PCC/MPFC/IPC acting as hubs in the DMN. Compared to old control, MPFC and IPC lost their roles as hubs owing to the obvious causal interaction disruption, and PCC was preserved as the only hub showing significant causal relations with all other nodes.

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

    Directory of Open Access Journals (Sweden)

    Idil Kokal

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

  9. Reasoning the causality of city sprawl, traffic congestion, and green land disappearance in Taiwan using the CLD model.

    Science.gov (United States)

    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.

  10. Testing a causal model of environmental influences on the early drug involvement of inner city junior high school youths.

    Science.gov (United States)

    Dembo, R; Farrow, D; Schmeidler, J; Burgos, W

    1979-01-01

    The present study examines a causal model explaining inner city youths' drug involvement using environmental variables which previously have been investigated singly or in various combinations and shown to influence drug use: the availability of drugs in the neighborhood and at school, a view of the neighborhood as tough, the esteem given to drug using, gang-involved persons by peers, friends' substance use, and participation in drug/street culture spare-time activities. The results show friends' use of alcohol and marijuana and participation in drug/street culture out-of-school activities have strong direct effects on personal drug involvement for the Black and Puerto Rican junior high school males and females who were studied; further, friends' use of alcohol and marijuana and the status peers give to drug using, gang-involved persons have respectable indirect effects on drug involvement for the four groups. In addition to these common features, a number of differences in the factors relating to drug involvement are found in the four groups. Implications of the results for alternative methods of drug abuse prevention and treatment are discussed, as is the necessity of utilizing an environmental, sociocultural view of drug use to adequately explain youth drug taking.

  11. Anterior cingulate cortico-hippocampal dysconnectivity in unaffected relatives of schizophrenia patients: a stochastic dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Yi-Bin Xi

    2016-07-01

    Full Text Available Familial risk plays a significant role in the etiology of schizophrenia (SZ. Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC, caudate, dorsolateral prefrontal cortex (DLPFC, and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients — according to the DSM-IV — were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI. We used stochastic dynamic causal modeling (sDCM to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ.

  12. Reasoning the Causality of City Sprawl, Traffic Congestion, and Green Land Disappearance in Taiwan Using the CLD Model

    Science.gov (United States)

    Chen, Mei-Chih; Chang, Kaowen

    2014-01-01

    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. PMID:25383609

  13. Classical planning and causal implicatures

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Benotti, Luciana

    for understanding the structure of task-oriented dialogues. Such dialogues locate conversational acts in contexts containing both pending tasks and the acts which bring them about. The ability to infer causal implicatures lets us interleave decisions about "how to sequence actions" with decisions about "when......In this paper we motivate and describe a dialogue manager (called Frolog) which uses classical planning to infer causal implicatures. A causal implicature is a type of Gricean relation implicature, a highly context dependent form of inference. As we shall see, causal implicatures are important...... to generate clarification requests"; as a result we can model task-oriented dialogue as an interactive process locally structured by negotiation of the underlying task. We give several examples of Frolog-human dialog, discuss the limitations imposed by the classical planning paradigm, and indicate...

  14. Causality in Science

    Directory of Open Access Journals (Sweden)

    Cristina Puente Águeda

    2011-10-01

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

  15. Causal Entropic Forces

    Science.gov (United States)

    Wissner-Gross, A. D.; Freer, C. E.

    2013-04-01

    Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.

  16. How people learn about causal influence when there are many possible causes: A model based on informative transitions.

    Science.gov (United States)

    Derringer, Cory; Rottman, Benjamin Margolin

    2018-05-01

    Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions - how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time ('one-change transitions') than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this 'Informative Transitions' learning processes. Copyright © 2018 Elsevier Inc. All rights reserved.

  17. Causality in Europeanization Research

    DEFF Research Database (Denmark)

    Lynggaard, Kennet

    2012-01-01

    to develop discursive institutional analytical frameworks and something that comes close to the formulation of hypothesis on the effects of European Union (EU) policies and institutions on domestic change. Even if these efforts so far do not necessarily amount to substantive theories or claims of causality...... of discursive causalities towards more substantive claims of causality between EU policy and institutional initiatives and domestic change....

  18. The enhanced information flow from visual cortex to frontal area facilitates SSVEP response: evidence from model-driven and data-driven causality analysis

    Science.gov (United States)

    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.

  19. A Causal Model of Linkages between Environment and Organizational Structure, and Its Performance Implications in International Service Distribution: An Empirical Study of Restaurant and Hotel Industry

    OpenAIRE

    Kim, Seehyung

    2005-01-01

    This research develops and tests a model of the service unit ownership and control patterns used by international service companies. The main purpose of this study is to investigate trivariate causal relationships among environmental factors, organizational structure, and perceived performance in the internationalization process of service firms. A service firm operating in foreign soil has a choice of three general entry mode strategies offering different degrees of ownership and control of ...

  20. Hierarchical organisation of causal graphs

    International Nuclear Information System (INIS)

    Dziopa, P.

    1993-01-01

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

  1. Tools for Detecting Causality in Space Systems

    Science.gov (United States)

    Johnson, J.; Wing, S.

    2017-12-01

    Complex systems such as the solar and magnetospheric envivonment often exhibit patterns of behavior that suggest underlying organizing principles. Causality is a key organizing principle that is particularly difficult to establish in strongly coupled nonlinear systems, but essential for understanding and modeling the behavior of systems. While traditional methods of time-series analysis can identify linear correlations, they do not adequately quantify the distinction between causal and coincidental dependence. We discuss tools for detecting causality including: granger causality, transfer entropy, conditional redundancy, and convergent cross maps. The tools are illustrated by applications to magnetospheric and solar physics including radiation belt, Dst (a magnetospheric state variable), substorm, and solar cycle dynamics.

  2. 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.

  3. A pedagogical walkthrough of computational modeling and simulation of Wnt signaling pathway using static causal models in MATLAB

    OpenAIRE

    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...

  4. Causal random geometry from stochastic quantization

    DEFF Research Database (Denmark)

    Ambjørn, Jan; Loll, R.; Westra, W.

    2010-01-01

     in this short note we review a recently found formulation of two-dimensional causal quantum gravity defined through Causal Dynamical Triangulations and stochastic quantization. This procedure enables one to extract the nonperturbative quantum Hamiltonian of the random surface model including the...... the sum over topologies. Interestingly, the generally fictitious stochastic time corresponds to proper time on the geometries...

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

    Science.gov (United States)

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

    Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

  6. Causality in Classical Physics

    Indian Academy of Sciences (India)

    IAS Admin

    Classical physics encompasses the study of phys- ical phenomena which range from local (a point) to nonlocal (a region) in space and/or time. We discuss the concept of spatial and temporal non- locality. However, one of the likely implications pertaining to nonlocality is non-causality. We study causality in the context of ...

  7. Causality in Classical Electrodynamics

    Science.gov (United States)

    Savage, Craig

    2012-01-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  9. mediation: R Package for Causal Mediation Analysis

    Directory of Open Access Journals (Sweden)

    Dustin Tingley

    2014-09-01

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

  10. Internal modeling of upcoming speech: A causal role of the right posterior cerebellum in non-motor aspects of language production.

    Science.gov (United States)

    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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma

    Science.gov (United States)

    Engelmann, Julia C.; Amann, Thomas; Ott-Rötzer, Birgitta; Nützel, Margit; Reinders, Yvonne; Reinders, Jörg; Thasler, Wolfgang E.; Kristl, Theresa; Teufel, Andreas; Huber, Christian G.; Oefner, Peter J.

    2015-01-01

    Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype. PMID:26020769

  12. Evaluating WAIS-IV structure through a different psychometric lens: structural causal model discovery as an alternative to confirmatory factor analysis.

    Science.gov (United States)

    van Dijk, Marjolein J A M; Claassen, Tom; Suwartono, Christiany; van der Veld, William M; van der Heijden, Paul T; Hendriks, Marc P H

    Since the publication of the WAIS-IV in the U.S. in 2008, efforts have been made to explore the structural validity by applying factor analysis to various samples. This study aims to achieve a more fine-grained understanding of the structure of the Dutch language version of the WAIS-IV (WAIS-IV-NL) by applying an alternative analysis based on causal modeling in addition to confirmatory factor analysis (CFA). The Bayesian Constraint-based Causal Discovery (BCCD) algorithm learns underlying network structures directly from data and assesses more complex structures than is possible with factor analysis. WAIS-IV-NL profiles of two clinical samples of 202 patients (i.e. patients with temporal lobe epilepsy and a mixed psychiatric outpatient group) were analyzed and contrasted with a matched control group (N = 202) selected from the Dutch standardization sample of the WAIS-IV-NL to investigate internal structure by means of CFA and BCCD. With CFA, the four-factor structure as proposed by Wechsler demonstrates acceptable fit in all three subsamples. However, BCCD revealed three consistent clusters (verbal comprehension, visual processing, and processing speed) in all three subsamples. The combination of Arithmetic and Digit Span as a coherent working memory factor could not be verified, and Matrix Reasoning appeared to be isolated. With BCCD, some discrepancies from the proposed four-factor structure are exemplified. Furthermore, these results fit CHC theory of intelligence more clearly. Consistent clustering patterns indicate these results are robust. The structural causal discovery approach may be helpful in better interpreting existing tests, the development of new tests, and aid in diagnostic instruments.

  13. Dynamics and causality constraints

    International Nuclear Information System (INIS)

    Sousa, Manoelito M. de

    2001-04-01

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

  14. How to Be Causal: Time, Spacetime and Spectra

    Science.gov (United States)

    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…

  15. Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model.

    Science.gov (United States)

    Padula, Amy M; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B

    2012-11-01

    Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000-2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.

  16. Causal beliefs about depression in different cultural groups – What do cognitive psychological theories of causal learning and reasoning predict?

    OpenAIRE

    York eHagmayer; Neele eEngelmann

    2014-01-01

    Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic lite...

  17. Perceptual causality in children.

    Science.gov (United States)

    Schlottmann, Anne; Allen, Deborah; Linderoth, Carina; Hesketh, Sarah

    2002-01-01

    Three experiments considered the development of perceptual causality in children from 3 to 9 years of age (N = 176 in total). Adults tend to see cause and effect even in schematic, two-dimensional motion events: Thus, if square A moves toward B, which moves upon contact, they report that A launches B--physical causality. If B moves before contact, adults report that B tries to escape from A--social or psychological causality. A brief pause between movements eliminates such impressions. Even infants in the first year of life are sensitive to causal structure in both contact and no-contact events, but previous research with talking-age children found poor verbal reports. The present experiments used a picture-based forced-choice task to reduce linguistic demands. Observers saw eight different animations involving squares A and B. Events varied in whether or not these agents made contact; whether or not there was a delay at the closest point; and whether they moved rigidly or with a rhythmic, nonrigid "caterpillar" motion. Participants of all ages assigned events with contact to the physical domain and events without contact to the psychological domain. In addition, participants of all ages chose causality more often for events without delay than with delay, but these events became more distinct over the preschool range. The manipulation of agent motion had only minor and inconsistent effects across studies, even though children of all ages considered only the nonrigid motion to be animal-like. These results agree with the view that perceptual causality is available early in development.

  18. Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor

    Directory of Open Access Journals (Sweden)

    Hae-Jeong Park

    2017-07-01

    Full Text Available Thalamotomy at the ventralis intermedius nucleus for essential tremor is known to cause changes in motor circuitry, but how a focal lesion leads to progressive changes in connectivity is not clear. To understand the mechanisms by which thalamotomy exerts enduring effects on motor circuitry, a quantitative analysis of directed or effective connectivity among motor-related areas is required. We characterized changes in effective connectivity of the motor system following thalamotomy using (spectral dynamic causal modeling (spDCM for resting-state fMRI. To differentiate long-lasting treatment effects from transient effects, and to identify symptom-related changes in effective connectivity, we subject longitudinal resting-state fMRI data to spDCM, acquired 1 day prior to, and 1 day, 7 days, and 3 months after thalamotomy using a non-cranium-opening MRI-guided focused ultrasound ablation technique. For the group-level (between subject analysis of longitudinal (between-session effects, we introduce a multilevel parametric empirical Bayes (PEB analysis for spDCM. We found remarkably selective and consistent changes in effective connectivity from the ventrolateral nuclei and the supplementary motor area to the contralateral dentate nucleus after thalamotomy, which may be mediated via a polysynaptic thalamic–cortical–cerebellar motor loop. Crucially, changes in effective connectivity predicted changes in clinical motor-symptom scores after thalamotomy. This study speaks to the efficacy of thalamotomy in regulating the dentate nucleus in the context of treating essential tremor. Furthermore, it illustrates the utility of PEB for group-level analysis of dynamic causal modeling in quantifying longitudinal changes in effective connectivity; i.e., measuring long-term plasticity in human subjects non-invasively.

  19. Regression to Causality

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  20. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

    Science.gov (United States)

    Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

    2016-03-01

    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

  1. Identifying Causality from Alarm Observations

    DEFF Research Database (Denmark)

    Kirchhübel, Denis; Zhang, Xinxin; Lind, Morten

    on an abstracted model of the mass and energy flows in the system. The application of MFM for root cause analysis based alarm grouping has been demonstrated and can be extended to reason about the direction of causality considering the entirety of the alarms present in the system for more comprehensive decision...... support. This contribution presents the foundation for combining the cause and consequence propagation of multiple observations from the system based on an MFM model. The proposed logical reasoning matches actually observed alarms to the propagation analysis in MFM to distinguish plausible causes...

  2. The Effects of a Model-Based Physics Curriculum Program with a Physics First Approach: A Causal-Comparative Study

    Science.gov (United States)

    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…

  3. Causality and Free Will

    Czech Academy of Sciences Publication Activity Database

    Hvorecký, Juraj

    2012-01-01

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

  4. The teacher, the physician and the person: exploring causal connections between teaching performance and role model types using directed acyclic graphs

    NARCIS (Netherlands)

    Boerebach, Benjamin C. M.; Lombarts, Kiki M. J. M. H.; Scherpbier, Albert J. J.; Arah, Onyebuchi A.

    2013-01-01

    In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the

  5. Ecological and cosmological coexistence thinking in a hypervariable environment: Causal models of economic success and failure among farmers, foragers, and fishermen of southwestern Madagascar

    Directory of Open Access Journals (Sweden)

    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

  6. Ecological and cosmological coexistence thinking in a hypervariable environment: causal models of economic success and failure among farmers, foragers, and fishermen of southwestern Madagascar.

    Science.gov (United States)

    Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte

    2015-01-01

    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-cultural analyses may

  7. Modeling of Interactions between the Zebrafish Hatching Enzyme ZHE1 and A Series of Metal Oxide Nanoparticles: Nano-QSAR and Causal Analysis of Inactivation Mechanisms

    Directory of Open Access Journals (Sweden)

    Natalia Sizochenko

    2017-10-01

    Full Text Available The quantitative relationships between the activity of zebrafish ZHE1 enzyme and a series of experimental and physicochemical features of 24 metal oxide nanoparticles were revealed. Vital characteristics of the nanoparticles’ structure were reflected using both experimental and theoretical descriptors. The developed quantitative structure–activity relationship model for nanoparticles (nano-QSAR was capable of predicting the enzyme inactivation based on four descriptors: the hydrodynamic radius, mass density, the Wigner–Seitz radius, and the covalent index. The nano-QSAR model was calculated using the non-linear regression tree M5P algorithm. The developed model is characterized by high robustness R2bagging = 0.90 and external predictivity Q2EXT = 0.93. This model is in agreement with modern theories of aquatic toxicity. Dissolution and size-dependent characteristics are among the key driving forces for enzyme inactivation. It was proven that ZnO, CuO, Cr2O3, and NiO nanoparticles demonstrated strong inhibitory effects because of their solubility. The proposed approach could be used as a non-experimental alternative to animal testing. Additionally, methods of causal discovery were applied to shed light on the mechanisms and modes of action.

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

    NARCIS (Netherlands)

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

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

  9. Space, time and causality

    International Nuclear Information System (INIS)

    Lucas, J.R.

    1984-01-01

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

  10. Operator ordering and causality

    OpenAIRE

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

    2011-01-01

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

  11. Test of a causal Human Resource Management-Performance Linkage Model: Evidence from the Greek manufacturing sector

    OpenAIRE

    Katou, A.; Katou, A.

    2011-01-01

    Although a number of studies have recognized the relationship between Human Resource Management (HRM) policies and organisational performance, the mechanisms through which HRM policies lead to organisational performance remain still unexplored. The purpose of this paper is to investigate the pathways leading from HRM policies to organisational performance by using structural equation modelling. Specifically, this analytical tool has been used to test a research framework that is constituted ...

  12. Causal relationships between energy consumption, foreign direct investment and economic growth: Fresh evidence from dynamic simultaneous-equations models

    International Nuclear Information System (INIS)

    Omri, Anis; Kahouli, Bassem

    2014-01-01

    This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990–2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on the growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we show mixed results about the interrelationship between energy consumption, FDI and economic growth. - Highlights: • We examine the energy–FDI–growth nexus for a global panel of 65 countries. • Dynamic simultaneous-equation panel data models are used to address this issue. • We also investigate this nexus for three sub-panels which are constructed based on the income level of countries. • We show mixed results about the interrelationship between the three variables

  13. Assessment of the population-level effectiveness of the Avahan HIV-prevention programme in South India: a preplanned, causal-pathway-based modelling analysis.

    Science.gov (United States)

    Pickles, Michael; Boily, Marie-Claude; Vickerman, Peter; Lowndes, Catherine M; Moses, Stephen; Blanchard, James F; Deering, Kathleen N; Bradley, Janet; Ramesh, Banadakoppa M; Washington, Reynold; Adhikary, Rajatashuvra; Mainkar, Mandar; Paranjape, Ramesh S; Alary, Michel

    2013-11-01

    Avahan, the India AIDS initiative of the Bill & Melinda Gates Foundation, was a large-scale, targeted HIV prevention intervention. We aimed to assess its overall effectiveness by estimating the number and proportion of HIV infections averted across Avahan districts, following the causal pathway of the intervention. We created a mathematical model of HIV transmission in high-risk groups and the general population using data from serial cross-sectional surveys (integrated behavioural and biological assessments, IBBAs) within a Bayesian framework, which we used to reproduce HIV prevalence trends in female sex workers and their clients, men who have sex with men, and the general population in 24 South Indian districts over the first 4 years (2004-07 or 2005-08 dependent on the district) and the full 10 years (2004-13) of the Avahan programme. We tested whether these prevalence trends were more consistent with self-reported increases in consistent condom use after the implementation of Avahan or with a counterfactual (assuming consistent condom use increased at slower, pre-Avahan rates) using a Bayes factor, which gave a measure of the strength of evidence for the effectiveness estimates. Using regression analysis, we extrapolated the prevention effect in the districts covered by IBBAs to all 69 Avahan districts. In 13 of 24 IBBA districts, modelling suggested medium to strong evidence for the large self-reported increase in consistent condom use since Avahan implementation. In the remaining 11 IBBA districts, the evidence was weaker, with consistent condom use generally already high before Avahan began. Roughly 32700 HIV infections (95% credibility interval 17900-61600) were averted over the first 4 years of the programme in the IBBA districts with moderate to strong evidence. Addition of the districts with weaker evidence increased this total to 62800 (32000-118000) averted infections, and extrapolation suggested that 202000 (98300-407000) infections were averted

  14. Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality

    Science.gov (United States)

    Ito, Sosuke

    2016-01-01

    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. PMID:27833120

  15. Beyond Markov: Accounting for independence violations in causal reasoning.

    Science.gov (United States)

    Rehder, Bob

    2018-03-06

    Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. 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...

  17. Causal interpretation of stochastic differential equations

    DEFF Research Database (Denmark)

    Sokol, Alexander; Hansen, Niels Richard

    2014-01-01

    We give a causal interpretation of stochastic differential equations (SDEs) by defining the postintervention SDE resulting from an intervention in an SDE. We show that under Lipschitz conditions, the solution to the postintervention SDE is equal to a uniform limit in probability of postintervention...... structural equation models based on the Euler scheme of the original SDE, thus relating our definition to mainstream causal concepts. We prove that when the driving noise in the SDE is a Lévy process, the postintervention distribution is identifiable from the generator of the SDE....

  18. Normalizing the causality between time series

    Science.gov (United States)

    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.

  19. Spatial hypersurfaces in causal set cosmology

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  20. The Causal Relationship between Financial Development and ...

    African Journals Online (AJOL)

    The study employs cointegration, vector error correction model and Granger causality test to ascertain causation between financial development and economic performance in Tanzania. Economic performance is measured by the real GDP, whereas proxies for financial development are: the ratio of money supply to nominal ...

  1. Growth and Mortality Outcomes for Different Antiretroviral Therapy Initiation Criteria in Children Ages 1-5 Years: A Causal Modeling Analysis.

    Science.gov (United States)

    Schomaker, Michael; Davies, Mary-Ann; Malateste, Karen; Renner, Lorna; Sawry, Shobna; N'Gbeche, Sylvie; Technau, Karl-Günter; Eboua, François; Tanser, Frank; Sygnaté-Sy, Haby; Phiri, Sam; Amorissani-Folquet, Madeleine; Cox, Vivian; Koueta, Fla; Chimbete, Cleophas; Lawson-Evi, Annette; Giddy, Janet; Amani-Bosse, Clarisse; Wood, Robin; Egger, Matthias; Leroy, Valeriane

    2016-03-01

    There is limited evidence regarding the optimal timing of initiating antiretroviral therapy (ART) in children. We conducted a causal modeling analysis in children ages 1-5 years from the International Epidemiologic Databases to Evaluate AIDS West/Southern-Africa collaboration to determine growth and mortality differences related to different CD4-based treatment initiation criteria, age groups, and regions. ART-naïve children of ages 12-59 months at enrollment with at least one visit before ART initiation and one follow-up visit were included. We estimated 3-year growth and cumulative mortality from the start of follow-up for different CD4 criteria using g-computation. About one quarter of the 5,826 included children was from West Africa (24.6%).The median (first; third quartile) CD4% at the first visit was 16% (11%; 23%), the median weight-for-age z-scores and height-for-age z-scores were -1.5 (-2.7; -0.6) and -2.5 (-3.5; -1.5), respectively. Estimated cumulative mortality was higher overall, and growth was slower, when initiating ART at lower CD4 thresholds. After 3 years of follow-up, the estimated mortality difference between starting ART routinely irrespective of CD4 count and starting ART if either CD4 count <750 cells/mm³ or CD4% <25% was 0.2% (95% CI = -0.2%; 0.3%), and the difference in the mean height-for-age z-scores of those who survived was -0.02 (95% CI = -0.04; 0.01). Younger children ages 1-2 and children in West Africa had worse outcomes. Our results demonstrate that earlier treatment initiation yields overall better growth and mortality outcomes, although we could not show any differences in outcomes between immediate ART and delaying until CD4 count/% falls below 750/25%.

  2. Anterior cingulate cortex-related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging

    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.

  3. Causal Relations Drive Young Children's Induction, Naming, and Categorization

    Science.gov (United States)

    Opfer, John E.; Bulloch, Megan J.

    2007-01-01

    A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young…

  4. 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.

  5. Re-thinking local causality

    NARCIS (Netherlands)

    Friederich, Simon

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

  6. Expert Causal Reasoning and Explanation.

    Science.gov (United States)

    Kuipers, Benjamin

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

  7. Causal aspects of diffraction

    International Nuclear Information System (INIS)

    Crawford, G.N.

    1981-01-01

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

  8. Clear message for causality

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-12-01

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

  9. Induced gamma-band brain responses to direct eye contact in the frontal and parietal cortices.

    Science.gov (United States)

    Iwaki, Sunao

    2013-01-01

    We used simultaneous recordings of the neuromagnetic (MEG) and electrooculogram (EOG) recordings on a pair of directly facing subjects, i.e., the sender and the observer of the eye gaze, to measure changes in the spontaneous brain activities while the observer perceives changes in eye gaze direction of the sender. The MEG signals were analyzed in the time-frequency domain to evaluate event-related changes in the spontaneous brain activities induced by the onset of eye movements. Significant increase in the gamma-band power was observed in the eye-contact condition compared to the averting condition in the right superior parietal, bilateral posterior superior-temporal, and the frontal areas of the observer. Together with the preliminary results from the frequency-domain Granger-Geweke causality analysis, the current results indicate that the connectivity between (a) the bilateral frontal areas, and (b) the right frontal and parietal areas might be crucial for the perception of eye gaze of the directly facing person. The increase in gamma-band activities in these regions might reflect the integration of information processed individually in these regions for eye gaze perception.

  10. Neural Correlates of Causal Power Judgments

    Directory of Open Access Journals (Sweden)

    Denise Dellarosa Cummins

    2014-12-01

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

  11. Growth and Mortality Outcomes for Different Antiretroviral Therapy Initiation Criteria in Children aged 1–5 Years: A Causal Modelling Analysis

    Science.gov (United States)

    Schomaker, Michael; Davies, Mary-Ann; Malateste, Karen; Renner, Lorna; Sawry, Shobna; N’Gbeche, Sylvie; Technau, Karl-Günter; Eboua, François; Tanser, Frank; Sygnaté-Sy, Haby; Phiri, Sam; Amorissani-Folquet, Madeleine; Cox, Vivian; Koueta, Fla; Chimbete, Cleophas; Lawson-Evi, Annette; Giddy, Janet; Amani-Bosse, Clarisse; Wood, Robin; Egger, Matthias; Leroy, Valeriane

    2017-01-01

    Background There is limited evidence regarding the optimal timing of initiating antiretroviral therapy (ART) in children. We conducted a causal modelling analysis in children aged 1–5 years from the International Epidemiologic Databases to Evaluate AIDS West/Southern-Africa collaboration to determine growth and mortality differences related to different CD4-based treatment initiation criteria, age groups and regions. Methods ART-naïve children of age 12–59 months at enrollment with at least one visit before ART initiation and one follow-up visit were included. We estimated 3-year growth and cumulative mortality from the start of follow-up for different CD4 criteria using g-computation. Results About one quarter of the 5826 included children was from West Africa (24.6%). The median (first; third quartile) CD4% at the first visit was 16% (11%;23%), the median weight-for-age z-scores and height-for-age z-scores were −1.5 (−2.7; −0.6) and −2.5 (−3.5; −1.5), respectively. Estimated cumulative mortality was higher overall, and growth was slower, when initiating ART at lower CD4 thresholds. After 3 years of follow-up, the estimated mortality difference between starting ART routinely irrespective of CD4 count and starting ART if either CD4 count<750 cells/mm3 or CD4%<25% was 0.2% (95%CI: −0.2%;0.3%), and the difference in the mean height-for-age z-scores of those who survived was −0.02 (95%CI: −0.04;0.01). Younger children aged 1–2 and children in West Africa had worse outcomes. Conclusions Our results demonstrate that earlier treatment initiation yields overall better growth and mortality outcomes, though we could not show any differences in outcomes between immediate ART and delaying until CD4 count/% falls below750/25%. PMID:26479876

  12. Principal stratification in causal inference.

    Science.gov (United States)

    Frangakis, Constantine E; Rubin, Donald B

    2002-03-01

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

  13. Causal boundary for stably causal space-times

    International Nuclear Information System (INIS)

    Racz, I.

    1987-12-01

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

  14. Functional equations with causal operators

    CERN Document Server

    Corduneanu, C

    2003-01-01

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

  15. Causality between Prices and Wages: VECM Analysis for EU-27

    Directory of Open Access Journals (Sweden)

    Adriatik Hoxha

    2010-09-01

    Full Text Available The literature on causality as well as the empirical evidence clearly shows that there are two opposing groups of economists, who support different hypotheses with respect to the flow of causality in the price-wage causal relationship. The first group argues that causality runs from wages to prices, whereas the second argues that effect flows from prices to wages. Nonetheless, the literature review suggeststhat there is at least some consensus in that researcher’s conclusions may be contingent on the type of data employed, applied econometric model, or even that relationship may alter with economic cycles. This paper empirically examines theprice-wage causal relationship in EU-27, by using the OLS and VECM analysis, and it also provides robust evidence in support of a bilateral causal relationship between prices and wages, both in long-run as well as in the shortrun.Prior to designing and estimating the econometric model we have performed stationarity tests for the employed price, wage and productivity variables. Additionally, we have also specified the model taking into account the lag order as well as the rank of co-integration for the co-integrated variables. Furthermore, we have also applied respective restrictions on the parameters of estimatedVECM. The evidence resulting from model robustness checks indicates that results are statistically robust. Although far from closing the issue of causality between prices and wages, this paper at least provides some fresh evidence in the case of EU-27.

  16. Statistical causal inferences and their applications in public health research

    CERN Document Server

    Wu, Pan; Chen, Ding-Geng

    2016-01-01

    This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

  17. Assessing statistical significance in causal graphs.

    Science.gov (United States)

    Chindelevitch, Leonid; Loh, Po-Ru; Enayetallah, Ahmed; Berger, Bonnie; Ziemek, Daniel

    2012-02-20

    Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs--directed graphs whose edges additionally have a sign denoting upregulation or downregulation--can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities; conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing. First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds.Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in- and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causal graphs

  18. Consciousness and the "Causal Paradox"

    OpenAIRE

    Velmans, Max

    1996-01-01

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

  19. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 4: IDAC causal model of operator problem-solving response

    International Nuclear Information System (INIS)

    Chang, Y.H.J.; Mosleh, A.

    2007-01-01

    This is the fourth in a series of five papers describing the Information, Decision, and Action in Crew context (IDAC) operator response model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model has been developed to probabilistically predicts the responses of a nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper assesses the effects of the performance-influencing factors (PIFs) affecting the operators' problem-solving responses including information pre-processing (I), diagnosis and decision making (D), and action execution (A). Literature support and justifications are provided for the assessment on the influences of PIFs

  20. Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors

    NARCIS (Netherlands)

    Hecq, Alain; Issler, J.V.; Telg, Sean

    2017-01-01

    The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into

  1. Causal beliefs about depression in different cultural groups-what do cognitive psychological theories of causal learning and reasoning predict?

    Science.gov (United States)

    Hagmayer, York; Engelmann, Neele

    2014-01-01

    Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given.

  2. Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

    Science.gov (United States)

    Hagmayer, York; Engelmann, Neele

    2014-01-01

    Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given. PMID:25505432

  3. Causal beliefs about depression in different cultural groups – What do cognitive psychological theories of causal learning and reasoning predict?

    Directory of Open Access Journals (Sweden)

    York eHagmayer

    2014-11-01

    Full Text Available Cognitive psychological research focusses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analysed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given.

  4. When to Start Antiretroviral Therapy in Children Aged 2–5 Years: A Collaborative Causal Modelling Analysis of Cohort Studies from Southern Africa

    Science.gov (United States)

    Schomaker, Michael; Egger, Matthias; Ndirangu, James; Phiri, Sam; Moultrie, Harry; Technau, Karl; Cox, Vivian; Giddy, Janet; Chimbetete, Cleophas; Wood, Robin; Gsponer, Thomas; Bolton Moore, Carolyn; Rabie, Helena; Eley, Brian; Muhe, Lulu; Penazzato, Martina; Essajee, Shaffiq; Keiser, Olivia; Davies, Mary-Ann

    2013-01-01

    Background There is limited evidence on the optimal timing of antiretroviral therapy (ART) initiation in children 2–5 y of age. We conducted a causal modelling analysis using the International Epidemiologic Databases to Evaluate AIDS–Southern Africa (IeDEA-SA) collaborative dataset to determine the difference in mortality when starting ART in children aged 2–5 y immediately (irrespective of CD4 criteria), as recommended in the World Health Organization (WHO) 2013 guidelines, compared to deferring to lower CD4 thresholds, for example, the WHO 2010 recommended threshold of CD4 count <750 cells/mm3 or CD4 percentage (CD4%) <25%. Methods and Findings ART-naïve children enrolling in HIV care at IeDEA-SA sites who were between 24 and 59 mo of age at first visit and with ≥1 visit prior to ART initiation and ≥1 follow-up visit were included. We estimated mortality for ART initiation at different CD4 thresholds for up to 3 y using g-computation, adjusting for measured time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score. Confidence intervals were constructed using bootstrapping. The median (first; third quartile) age at first visit of 2,934 children (51% male) included in the analysis was 3.3 y (2.6; 4.1), with a median (first; third quartile) CD4 count of 592 cells/mm3 (356; 895) and median (first; third quartile) CD4% of 16% (10%; 23%). The estimated cumulative mortality after 3 y for ART initiation at different CD4 thresholds ranged from 3.4% (95% CI: 2.1–6.5) (no ART) to 2.1% (95% CI: 1.3%–3.5%) (ART irrespective of CD4 value). Estimated mortality was overall higher when initiating ART at lower CD4 values or not at all. There was no mortality difference between starting ART immediately, irrespective of CD4 value, and ART initiation at the WHO 2010 recommended threshold of CD4 count <750 cells/mm3 or CD4% <25%, with mortality estimates of 2.1% (95% CI: 1.3%–3.5%) and 2.2% (95% CI: 1.4%–3.5%) after 3 y, respectively. The

  5. When to start antiretroviral therapy in children aged 2-5 years: a collaborative causal modelling analysis of cohort studies from southern Africa.

    Science.gov (United States)

    Schomaker, Michael; Egger, Matthias; Ndirangu, James; Phiri, Sam; Moultrie, Harry; Technau, Karl; Cox, Vivian; Giddy, Janet; Chimbetete, Cleophas; Wood, Robin; Gsponer, Thomas; Bolton Moore, Carolyn; Rabie, Helena; Eley, Brian; Muhe, Lulu; Penazzato, Martina; Essajee, Shaffiq; Keiser, Olivia; Davies, Mary-Ann

    2013-11-01

    There is limited evidence on the optimal timing of antiretroviral therapy (ART) initiation in children 2-5 y of age. We conducted a causal modelling analysis using the International Epidemiologic Databases to Evaluate AIDS-Southern Africa (IeDEA-SA) collaborative dataset to determine the difference in mortality when starting ART in children aged 2-5 y immediately (irrespective of CD4 criteria), as recommended in the World Health Organization (WHO) 2013 guidelines, compared to deferring to lower CD4 thresholds, for example, the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3) or CD4 percentage (CD4%) <25%. ART-naïve children enrolling in HIV care at IeDEA-SA sites who were between 24 and 59 mo of age at first visit and with ≥1 visit prior to ART initiation and ≥1 follow-up visit were included. We estimated mortality for ART initiation at different CD4 thresholds for up to 3 y using g-computation, adjusting for measured time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score. Confidence intervals were constructed using bootstrapping. The median (first; third quartile) age at first visit of 2,934 children (51% male) included in the analysis was 3.3 y (2.6; 4.1), with a median (first; third quartile) CD4 count of 592 cells/mm(3) (356; 895) and median (first; third quartile) CD4% of 16% (10%; 23%). The estimated cumulative mortality after 3 y for ART initiation at different CD4 thresholds ranged from 3.4% (95% CI: 2.1-6.5) (no ART) to 2.1% (95% CI: 1.3%-3.5%) (ART irrespective of CD4 value). Estimated mortality was overall higher when initiating ART at lower CD4 values or not at all. There was no mortality difference between starting ART immediately, irrespective of CD4 value, and ART initiation at the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3) or CD4% <25%, with mortality estimates of 2.1% (95% CI: 1.3%-3.5%) and 2.2% (95% CI: 1.4%-3.5%) after 3 y, respectively. The analysis was limited by loss to follow

  6. When to start antiretroviral therapy in children aged 2-5 years: a collaborative causal modelling analysis of cohort studies from southern Africa.

    Directory of Open Access Journals (Sweden)

    Michael Schomaker

    2013-11-01

    Full Text Available There is limited evidence on the optimal timing of antiretroviral therapy (ART initiation in children 2-5 y of age. We conducted a causal modelling analysis using the International Epidemiologic Databases to Evaluate AIDS-Southern Africa (IeDEA-SA collaborative dataset to determine the difference in mortality when starting ART in children aged 2-5 y immediately (irrespective of CD4 criteria, as recommended in the World Health Organization (WHO 2013 guidelines, compared to deferring to lower CD4 thresholds, for example, the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3 or CD4 percentage (CD4% <25%.ART-naïve children enrolling in HIV care at IeDEA-SA sites who were between 24 and 59 mo of age at first visit and with ≥1 visit prior to ART initiation and ≥1 follow-up visit were included. We estimated mortality for ART initiation at different CD4 thresholds for up to 3 y using g-computation, adjusting for measured time-dependent confounding of CD4 percent, CD4 count, and weight-for-age z-score. Confidence intervals were constructed using bootstrapping. The median (first; third quartile age at first visit of 2,934 children (51% male included in the analysis was 3.3 y (2.6; 4.1, with a median (first; third quartile CD4 count of 592 cells/mm(3 (356; 895 and median (first; third quartile CD4% of 16% (10%; 23%. The estimated cumulative mortality after 3 y for ART initiation at different CD4 thresholds ranged from 3.4% (95% CI: 2.1-6.5 (no ART to 2.1% (95% CI: 1.3%-3.5% (ART irrespective of CD4 value. Estimated mortality was overall higher when initiating ART at lower CD4 values or not at all. There was no mortality difference between starting ART immediately, irrespective of CD4 value, and ART initiation at the WHO 2010 recommended threshold of CD4 count <750 cells/mm(3 or CD4% <25%, with mortality estimates of 2.1% (95% CI: 1.3%-3.5% and 2.2% (95% CI: 1.4%-3.5% after 3 y, respectively. The analysis was limited by loss to follow-up and

  7. Informational and Causal Architecture of Continuous-time Renewal Processes

    Science.gov (United States)

    Marzen, Sarah; Crutchfield, James P.

    2017-07-01

    We introduce the minimal maximally predictive models (ɛ {-machines }) of processes generated by certain hidden semi-Markov models. Their causal states are either discrete, mixed, or continuous random variables and causal-state transitions are described by partial differential equations. As an application, we present a complete analysis of the ɛ {-machines } of continuous-time renewal processes. This leads to closed-form expressions for their entropy rate, statistical complexity, excess entropy, and differential information anatomy rates.

  8. Causal influence in linear Langevin networks without feedback

    Science.gov (United States)

    Auconi, Andrea; Giansanti, Andrea; Klipp, Edda

    2017-04-01

    The intuition of causation is so fundamental that almost every research study in life sciences refers to this concept. However, a widely accepted formal definition of causal influence between observables is still missing. In the framework of linear Langevin networks without feedback (linear response models) we propose a measure of causal influence based on a new decomposition of information flows over time. We discuss its main properties and we compare it with other information measures like the transfer entropy. We are currently unable to extend the definition of causal influence to systems with a general feedback structure and nonlinearities.

  9. Causality and prediction: differences and points of contact

    Directory of Open Access Journals (Sweden)

    Luis Carlos Silva Ayçaguer, PhD

    2014-09-01

    Full Text Available This contribution presents the differences between those variables that might play a causal role in a certain process and those only valuable for predicting the outcome. Some considerations are made about the core intervention of the association and the temporal precedence and biases in both cases, the study of causality and predictive modeling. In that context, several relevant aspects related to the design of the corresponding studies are briefly reviewed and some of the mistakes that are often committed in handling both, causality and prediction, are illustrated.

  10. Causality and analyticity in optics

    International Nuclear Information System (INIS)

    Nussenzveig, H.M.

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

  11. A Causal Rhythm Grouping

    DEFF Research Database (Denmark)

    Jensen, Karl Kristoffer

    2005-01-01

    This paper presents a method to identify segment boundaries in music. The method is based on a multi-step model; first a features is measured from the audio, then a measure of rhythm is calculated from the feature, the diagonal of a self-similarity matrix is calculated, and finally the segment...... boundaries are found on a smoothed novelty measure, calculated from the self-similarity matrix. All the steps of the model have been accompanied with an informal evaluation, and the final system is tested on a variety of rhythmic songs with good results. The paper introduces a new feature that is shown...

  12. Entropy for theories with indefinite causal structure

    International Nuclear Information System (INIS)

    Markes, Sonia; Hardy, Lucien

    2011-01-01

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

  13. Quantifying 'causality' in complex systems: understanding transfer entropy.

    Directory of Open Access Journals (Sweden)

    Fatimah Abdul Razak

    Full Text Available 'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of 'causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.

  14. A Causal Theory of Modality

    Directory of Open Access Journals (Sweden)

    José Tomás Alvarado

    2009-08-01

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

  15. Quantum theory and local causality

    CERN Document Server

    Hofer-Szabó, Gábor

    2018-01-01

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

  16. A Complex Systems Approach to Causal Discovery in Psychiatry.

    Directory of Open Access Journals (Sweden)

    Glenn N Saxe

    Full Text Available Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study. Next, it was applied to a much larger dataset of traumatized children (replication study. Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment. The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro and high-level (macro insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

  17. Tracking the evolution of causal cognition in humans.

    Science.gov (United States)

    Lombard, Marlize; Gärdenfors, Peter

    2017-12-30

    We suggest a seven-grade model for the evolution of causal cognition as a framework that can be used to gauge variation in the complexity of causal reasoning from the panin-hominin split until the appearance of cognitively modern hunter-gatherer communities. The intention is to put forward a cohesive model for the evolution of causal cognition in humans, which can be assessed against increasingly fine-grained empirical data from the palaeoanthropological and archaeological records. We propose that the tracking behaviour (i.e., the ability to interpret and follow external, inanimate, visual clues of hominins) provides a rich case study for tracing the evolution of causal cognition in our lineage. The grades of causal cognition are tentatively linked to aspects of the Stone Age/Palaeolithic archaeological record. Our model can also be applied to current work in evolutionary psychology and research on causal cognition, so that an inter-disciplinary understanding and correlation of processes becomes increasingly possible.

  18. Causal feedbacks in climate change

    NARCIS (Netherlands)

    Nes, van E.H.; Scheffer, M.; Brovkin, V.; Lenton, T.M.; Ye, H.; Deyle, E.; Sugihara, G.

    2015-01-01

    The statistical association between temperature and greenhouse gases over glacial cycles is well documented1, but causality behind this correlation remains difficult to extract directly from the data. A time lag of CO2 behind Antarctic temperature—originally thought to hint at a driving role for

  19. Granger Causality and Unit Roots

    DEFF Research Database (Denmark)

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

    2014-01-01

    , eventually rejecting the null hypothesis, even when the series are independent of each other. Moreover, controlling for these deterministic elements (in the auxiliary regressions of the test) does not preclude the possibility of drawing erroneous inferences. Granger-causality tests should not be used under...... stochastic nonstationarity, a property typically found in many macroeconomic variables....

  20. QED representation for the net of causal loops

    Science.gov (United States)

    Ciolli, Fabio; Ruzzi, Giuseppe; Vasselli, Ezio

    2015-06-01

    The present work tackles the existence of local gauge symmetries in the setting of Algebraic Quantum Field Theory (AQFT). The net of causal loops, previously introduced by the authors, is a model independent construction of a covariant net of local C*-algebras on any 4-dimensional globally hyperbolic space-time, aimed to capture structural properties of any reasonable quantum gauge theory. Representations of this net can be described by causal and covariant connection systems, and local gauge transformations arise as maps between equivalent connection systems. The present paper completes these abstract results, realizing QED as a representation of the net of causal loops in Minkowski space-time. More precisely, we map the quantum electromagnetic field Fμν, not free in general, into a representation of the net of causal loops and show that the corresponding connection system and the local gauge transformations find a counterpart in terms of Fμν.

  1. Impact of echinocandin on prognosis of proven invasive candidiasis in ICU: A post-hoc causal inference model using the AmarCAND2 study.

    Science.gov (United States)

    Bailly, Sébastien; Leroy, Olivier; Azoulay, Elie; Montravers, Philippe; Constantin, Jean-Michel; Dupont, Hervé; Guillemot, Didier; Lortholary, Olivier; Mira, Jean-Paul; Perrigault, Pierre-François; Gangneux, Jean-Pierre; Timsit, Jean-François

    2017-04-01

    guidelines recommend first-line systemic antifungal therapy (SAT) with echinocandins in invasive candidiasis (IC), especially in critically ill patients. This study aimed at assessing the impact of echinocandins compared to azoles as initial SAT on the 28-day prognosis in adult ICU patients. From the prospective multicenter AmarCAND2 cohort (835 patients), we selected those with documented IC and treated with echinocandins (ECH) or azoles (AZO). The average causal effect of echinocandins on 28-day mortality was assessed using an inverse probability of treatment weight (IPTW) estimator. 397 patients were selected, treated with echinocandins (242 patients, 61%) or azoles (155 patients, 39%); septic shock: 179 patients (45%). The median SAPSII was higher in the ECH group (48 [35; 62] vs. 43 [31; 58], p = 0.01). Crude mortality was 34% (ECH group) vs. 25% (AZO group). After adjustment on baseline confounders, no significant association emerged between initial SAT with echinocandins and 28-day mortality (HR: 0.95; 95% CI: [0.60; 1.49]; p = 0.82). However, echinocandin tended to benefit patients with septic shock (HR: 0.46 [0.19; 1.07]; p = 0.07). Patients who received echinocandins were more severely ill. Echinocandin use was associated with a non-significant 7% decrease of 28-day mortality and a trend to a beneficial effect for patient with septic shock. Copyright © 2017 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

  2. A study in cosmology and causal thermodynamics

    International Nuclear Information System (INIS)

    Oliveira, H.P. de.

    1986-01-01

    The especial relativity of thermodynamic theories for reversible and irreversible processes in continuous medium is studied. The formalism referring to equilibrium and non-equilibrium configurations, and theories which includes the presence of gravitational fields are discussed. The nebular model in contraction with dissipative processes identified by heat flux and volumetric viscosity is thermodymically analysed. This model is presented by a plane conformal metric. The temperature, pressure, entropy and entropy production within thermodynamic formalism which adopts the hypothesis of local equilibrium, is calculated. The same analysis is carried out considering a causal thermodynamics, which establishes a local entropy of non-equilibrium. Possible homogeneous and isotropic cosmological models, considering the new phenomenological equation for volumetric viscosity deriving from cause thermodynamics are investigated. The found out models have plane spatial section (K=0) and some ones do not have singularities. The energy conditions are verified and the entropy production for physically reasobable models are calculated. (M.C.K.) [pt

  3. Causal quantum theory and the collapse locality loophole

    International Nuclear Information System (INIS)

    Kent, Adrian

    2005-01-01

    Causal quantum theory is an umbrella term for ordinary quantum theory modified by two hypotheses: state vector reduction is a well-defined process, and strict local causality applies. The first of these holds in some versions of Copenhagen quantum theory and need not necessarily imply practically testable deviations from ordinary quantum theory. The second implies that measurement events which are spacelike separated have no nonlocal correlations. To test this prediction, which sharply differs from standard quantum theory, requires a precise definition of state vector reduction. Formally speaking, any precise version of causal quantum theory defines a local hidden variable theory. However, causal quantum theory is most naturally seen as a variant of standard quantum theory. For that reason it seems a more serious rival to standard quantum theory than local hidden variable models relying on the locality or detector efficiency loopholes. Some plausible versions of causal quantum theory are not refuted by any Bell experiments to date, nor is it evident that they are inconsistent with other experiments. They evade refutation via a neglected loophole in Bell experiments--the collapse locality loophole--which exists because of the possible time lag between a particle entering a measurement device and a collapse taking place. Fairly definitive tests of causal versus standard quantum theory could be made by observing entangled particles separated by ≅0.1 light seconds

  4. Two roads to noncommutative causality

    International Nuclear Information System (INIS)

    Besnard, Fabien

    2015-01-01

    We review the physical motivations and the mathematical results obtained so far in the isocone-based approach to noncommutative causality. We also give a briefer account of the alternative framework of Franco and Eckstein which is based on Lorentzian spectral triples. We compare the two theories on the simple example of the product geometry of the Minkowski plane by the finite noncommutative space with algebra M 2 (C). (paper)

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

    Science.gov (United States)

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

    2018-02-25

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

  6. Concept of statistical causality and local martingales

    Directory of Open Access Journals (Sweden)

    Valjarević Dragana

    2016-01-01

    Full Text Available In this paper we consider a statistical concept of causality in continuous time in filtered probability spaces which is based on Granger's definitions of causality. The given causality concept is closely connected to the preservation of the property being a local martingale if the filtration is getting larger. Namely, the local martingale remains unpredictable if the amount of information is increased. We proved that the preservation of this property is equivalent with the concept of causality.

  7. Obesity and infection: reciprocal causality.

    Science.gov (United States)

    Hainer, V; Zamrazilová, H; Kunešová, M; Bendlová, B; Aldhoon-Hainerová, I

    2015-01-01

    Associations between different infectious agents and obesity have been reported in humans for over thirty years. In many cases, as in nosocomial infections, this relationship reflects the greater susceptibility of obese individuals to infection due to impaired immunity. In such cases, the infection is not related to obesity as a causal factor but represents a complication of obesity. In contrast, several infections have been suggested as potential causal factors in human obesity. However, evidence of a causal linkage to human obesity has only been provided for adenovirus 36 (Adv36). This virus activates lipogenic and proinflammatory pathways in adipose tissue, improves insulin sensitivity, lipid profile and hepatic steatosis. The E4orf1 gene of Adv36 exerts insulin senzitizing effects, but is devoid of its pro-inflammatory modalities. The development of a vaccine to prevent Adv36-induced obesity or the use of E4orf1 as a ligand for novel antidiabetic drugs could open new horizons in the prophylaxis and treatment of obesity and diabetes. More experimental and clinical studies are needed to elucidate the mutual relations between infection and obesity, identify additional infectious agents causing human obesity, as well as define the conditions that predispose obese individuals to specific infections.

  8. P3-10: Crossmodal Perceptual Grouping Modulates Subjective Causality between Action and Outcome

    Directory of Open Access Journals (Sweden)

    Takahiro Kawabe

    2012-10-01

    Full Text Available Agents have to determine which external events their action has causally produced. A sensation of causal relation between action and outcome is called subjective causality. Subjective causality has been linked to the comparator model. This model assumes that the brain compares an internal prediction for action outcome with an actual sensory outcome, distinguishing between self and externally produced outcomes depending on spatiotemporal congruency. However, recent studies have expressed some doubt about the idea that subjective causality arises depending solely on the spatiotemporal congruency, suggesting instead that other perceptual/cognitive factors play a critical role in determining subjective causality. We hypothesized that crossmodal grouping between action and outcome contributed to subjective causality. Crossmodal temporal grouping is an essential factor for crossmodal simultaneity judgments with ungrouped crossmodal signals likely to be judged as non-simultaneous. We predicted that subjective causality would decrease when an agent's action was not temporally grouped with action outcome. In the experiment, observers were asked to press a key in order to trigger a display change with some temporal delay. To disrupt temporal grouping between action and outcome, a task-irrelevant visual flash or tone was sometimes presented synchronously with the button press and/or the display change. Subjective causality was decreased when the flash or the tone was coincided with the button press. This demonstrates that perceptual grouping has a key role in determination of subjective causality, a result that is not accounted for by the standard comparator model.

  9. Causal knowledge and reasoning in decision making

    NARCIS (Netherlands)

    Hagmayer, Y.; Witteman, C.L.M.

    2017-01-01

    Normative causal decision theories argue that people should use their causal knowledge in decision making. Based on these ideas, we argue that causal knowledge and reasoning may support and thereby potentially improve decision making based on expected outcomes, narratives, and even cues. We will

  10. The argumentative impact of causal relations

    DEFF Research Database (Denmark)

    Nielsen, Anne Ellerup

    1996-01-01

    such as causality, explanation and justification. In certain types of discourse, causal relations also imply an intentional element. This paper describes the way in which the semantic and pragmatic functions of causal markers can be accounted for in terms of linguistic and rhetorical theories of argumentation....

  11. Capturing connectivity and causality in complex industrial processes

    CERN Document Server

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

    2014-01-01

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

  12. A Causal Inference Analysis of the Effect of Wildland Fire ...

    Science.gov (United States)

    Wildfire smoke is a major contributor to ambient air pollution levels. In this talk, we develop a spatio-temporal model to estimate the contribution of fire smoke to overall air pollution in different regions of the country. We combine numerical model output with observational data within a causal inference framework. Our methods account for aggregation and potential bias of the numerical model simulation, and address uncertainty in the causal estimates. We apply the proposed method to estimation of ozone and fine particulate matter from wildland fires and the impact on health burden assessment. We develop a causal inference framework to assess contributions of fire to ambient PM in the presence of spatial interference.

  13. Corporate Governance and Financial Performance Nexus: Any Bidirectional Causality?

    Directory of Open Access Journals (Sweden)

    Alley Ibrahim S.

    2016-06-01

    Full Text Available Most studies on corporate governance recognize endogeneity in the nexus between corporate governance and financial performance. Little attention has, however, been paid to the direction of causality between the two phenomena, and hence the Vector Error Correction (VEC model, which allows for endogenous determination of the direction of causality, has not been widely employed. This study fills that gap by estimating the nexus and the direction of causality using the VEC model to analyze panel data on selected listed firms in Nigeria. The results agree with the findings of most previous studies that corporate governance significantly affects financial performance. Board skills, board composition and management skills enhanced financial performance indicators – return on equity (ROE, return on asset (ROA and net profit margin (NPM; in many occasions, significantly. Board size and audit committee size did not, and can actually undermine financial performance. More importantly, financial performance did not significantly affect corporate governance. On the basis of the lag structure of the VEC model, this study affirms unidirectional causality in the nexus, running from corporate governance to financial performance, nullifying the hypothesis of bidirectional causality in the nexus.

  14. Formalizing Neurath's ship: Approximate algorithms for online causal learning.

    Science.gov (United States)

    Bramley, Neil R; Dayan, Peter; Griffiths, Thomas L; Lagnado, David A

    2017-04-01

    Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  15. Causal Inference in the Perception of Verticality.

    Science.gov (United States)

    de Winkel, Ksander N; Katliar, Mikhail; Diers, Daniel; Bülthoff, Heinrich H

    2018-04-03

    The perceptual upright is thought to be constructed by the central nervous system (CNS) as a vector sum; by combining estimates on the upright provided by the visual system and the body's inertial sensors with prior knowledge that upright is usually above the head. Recent findings furthermore show that the weighting of the respective sensory signals is proportional to their reliability, consistent with a Bayesian interpretation of a vector sum (Forced Fusion, FF). However, violations of FF have also been reported, suggesting that the CNS may rely on a single sensory system (Cue Capture, CC), or choose to process sensory signals based on inferred signal causality (Causal Inference, CI). We developed a novel alternative-reality system to manipulate visual and physical tilt independently. We tasked participants (n = 36) to indicate the perceived upright for various (in-)congruent combinations of visual-inertial stimuli, and compared models based on their agreement with the data. The results favor the CI model over FF, although this effect became unambiguous only for large discrepancies (±60°). We conclude that the notion of a vector sum does not provide a comprehensive explanation of the perception of the upright, and that CI offers a better alternative.

  16. Norms and customs: causally important or causally impotent?

    Science.gov (United States)

    Jones, Todd

    2010-01-01

    In this article, I argue that norms and customs, despite frequently being described as being causes of behavior in the social sciences and ordinary conversation, cannot really cause behavior. Terms like "norms" and the like seem to refer to philosophically disreputable disjunctive properties. More problematically, even if they do not, or even if there can be disjunctive properties after all, I argue that norms and customs still cannot cause behavior. The social sciences would be better off without referring to properties like norms and customs as if they could be causal.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  18. Weighing the causal pies in case-control studies.

    Science.gov (United States)

    Liao, Shu-Fen; Lee, Wen-Chung

    2010-07-01

    Epidemiologists are familiar with the concepts of Rothman's causal pies. Using real data the Hoffman study showed recently how to calculate the "proportion of diseased subjects who develop the disease due to classes of sufficient causes" (PDCs). The PDC is actually an attributable-fraction index. It may be specific to a particular risk factor profile but it does not correspond to any given class of causal pies. In this study, we show how to estimate the "causal-pie weights" (CPWs), so that each and every class of causal pies has one and only one CPW attached to it. To conform to Rothman's model, we apply a non-negative linear odds model to constrain all the odds ratios (ORs) to be equal to or greater than one, and the interactions between them to be additive or superadditive. Based on these constrained ORs, we calculate the population attributable fractions, and then the CPWs. We used a published case-control data to show the methodology. The CPWs succinctly quantify the relative importance of different classes of causal pies. The proposed method helps to clarify the multi-factorial and complex interactive effects in disease causation. It also provides important information for designing an efficient public health intervention strategy. 2010 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  20. Informational and Causal Architecture of Discrete-Time Renewal Processes

    Directory of Open Access Journals (Sweden)

    Sarah E. Marzen

    2015-07-01

    Full Text Available Renewal processes are broadly used to model stochastic behavior consisting of isolated events separated by periods of quiescence, whose durations are specified by a given probability law. Here, we identify the minimal sufficient statistic for their prediction (the set of causal states, calculate the historical memory capacity required to store those states (statistical complexity, delineate what information is predictable (excess entropy, and decompose the entropy of a single measurement into that shared with the past, future, or both. The causal state equivalence relation defines a new subclass of renewal processes with a finite number of causal states despite having an unbounded interevent count distribution. We use the resulting formulae to analyze the output of the parametrized Simple Nonunifilar Source, generated by a simple two-state hidden Markov model, but with an infinite-state ϵ-machine presentation. All in all, the results lay the groundwork for analyzing more complex processes with infinite statistical complexity and infinite excess entropy.

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

    Science.gov (United States)

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

    2014-07-01

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

  2. Random number generators and causality

    International Nuclear Information System (INIS)

    Larrondo, H.A.; Martin, M.T.; Gonzalez, C.M.; Plastino, A.; Rosso, O.A.

    2006-01-01

    We advance a prescription to randomize physical or algorithmic Random Number Generators (RNG's) that do not pass Marsaglia's DIEHARD test suite and discuss a special physical quantifier, based on an intensive statistical complexity measure, that is able to adequately assess the improvements produced thereby. Eight RNG's are evaluated and the associated results are compared to those obtained by recourse to Marsaglia's DIEHARD test suite. Our quantifier, which is evaluated using causality arguments, can forecast whether a given RNG will pass the above mentioned test

  3. Semi-Automated Curation Allows Causal Network Model Building for the Quantification of Age-Dependent Plaque Progression in ApoE-/-Mouse.

    Science.gov (United States)

    Szostak, Justyna; Martin, Florian; Talikka, Marja; Peitsch, Manuel C; Hoeng, Julia

    2016-01-01

    The cellular and molecular mechanisms behind the process of atherosclerotic plaque destabilization are complex, and molecular data from aortic plaques are difficult to interpret. Biological network models may overcome these difficulties and precisely quantify the molecular mechanisms impacted during disease progression. The atherosclerosis plaque destabilization biological network model was constructed with the semiautomated curation pipeline, BELIEF. Cellular and molecular mechanisms promoting plaque destabilization or rupture were captured in the network model. Public transcriptomic data sets were used to demonstrate the specificity of the network model and to capture the different mechanisms that were impacted in ApoE -/- mouse aorta at 6 and 32 weeks. We concluded that network models combined with the network perturbation amplitude algorithm provide a sensitive, quantitative method to follow disease progression at the molecular level. This approach can be used to investigate and quantify molecular mechanisms during plaque progression.

  4. CADDIS Volume 1. Stressor Identification: About Causal Assessment

    Science.gov (United States)

    An introduction to the history of our approach to causal assessment, A chronology of causal history and philosophy, An introduction to causal history and philosophy, References for the Causal Assessment Background section of Stressor Identification

  5. Evidence for online processing during causal learning.

    Science.gov (United States)

    Liu, Pei-Pei; Luhmann, Christian C

    2015-03-01

    Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.

  6. Probabilistic causality and radiogenic cancers

    International Nuclear Information System (INIS)

    Groeer, P.G.

    1986-01-01

    A review and scrutiny of the literature on probability and probabilistic causality shows that it is possible under certain assumptions to estimate the probability that a certain type of cancer diagnosed in an individual exposed to radiation prior to diagnosis was caused by this exposure. Diagnosis of this causal relationship like diagnosis of any disease - malignant or not - requires always some subjective judgments by the diagnostician. It is, therefore, illusory to believe that tables based on actuarial data can provide objective estimates of the chance that a cancer diagnosed in an individual is radiogenic. It is argued that such tables can only provide a base from which the diagnostician(s) deviate in one direction or the other according to his (their) individual (consensual) judgment. Acceptance of a physician's diagnostic judgment by patients is commonplace. Similar widespread acceptance of expert judgment by claimants in radiation compensation cases does presently not exist. Judicious use of the present radioepidemiological tables prepared by the Working Group of the National Institutes of Health or of updated future versions of similar tables may improve the situation. 20 references

  7. Space and time in perceptual causality

    Directory of Open Access Journals (Sweden)

    Benjamin Straube

    2010-04-01

    Full Text Available Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte’s view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks.

  8. The Functions of Danish Causal Conjunctions

    Directory of Open Access Journals (Sweden)

    Rita Therkelsen

    2004-01-01

    Full Text Available In the article I propose an analysis of the Danish causal conjunctions fordi, siden and for based on the framework of Danish Functional Grammar. As conjunctions they relate two clauses, and their semantics have in common that it indicates a causal relationship between the clauses. The causal conjunctions are different as far as their distribution is concerned; siden conjoins a subordinate clause and a main clause, for conjoins two main clauses, and fordi is able to do both. Methodologically I have based my analysis on these distributional properties comparing siden and fordi conjoining a subordinate and a main clause, and comparing for and fordi conjoining two main clauses, following the thesis that they would establish a causal relationship between different kinds of content. My main findings are that fordi establishes a causal relationship between the events referred to by the two clauses, and the whole utterance functions as a statement of this causal relationship. Siden presupposes such a general causal relationship between the two events and puts forward the causing event as a reason for assuming or wishing or ordering the caused event, siden thus establishes a causal relationship between an event and a speech act. For equally presupposes a general causal relationship between two events and it establishes a causal relationship between speech acts, and fordi conjoining two main clauses is able to do this too, but in this position it also maintains its event-relating ability, the interpretation depending on contextual factors.

  9. Cognitive ageing on latent constructs for visual processing capacity: A novel Structural Equation Modelling framework with causal assumptions based on A Theory of Visual Attention

    Directory of Open Access Journals (Sweden)

    Simon eNielsen

    2015-01-01

    Full Text Available We examined the effects of normal ageing on visual cognition in a sample of 112 healthy adults aged 60-75. A testbattery was designed to capture high-level measures of visual working memory and low-level measures of visuospatial attention and memory. To answer questions of how cognitive ageing affects specific aspects of visual processing capacity, we used confirmatory factor analyses in Structural Equation Modelling (SEM; Model 2, informed by functional structures that were modelled with path analyses in SEM (Model 1. The results show that ageing effects were selective to measures of visual processing speed compared to visual short-term memory (VSTM capacity (Model 2. These results are consistent with some studies reporting selective ageing effects on processing speed, and inconsistent with other studies reporting ageing effects on both processing speed and VSTM capacity. In the discussion we argue that this discrepancy may be mediated by differences in age ranges, and variables of demography. The study demonstrates that SEM is a sensitive method to detect cognitive ageing effects even within a narrow age-range, and a useful approach to structure the relationships between measured variables, and the cognitive functional foundation they supposedly represent.

  10. Inferring connectivity in networked dynamical systems: Challenges using Granger causality

    Science.gov (United States)

    Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan

    2016-09-01

    Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

  11. On the Temporal Causal Relationship Between Macroeconomic Variables

    Directory of Open Access Journals (Sweden)

    Srinivasan Palamalai

    2014-02-01

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

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

    International Nuclear Information System (INIS)

    Sung, Bongsuk; Song, Woo-Yong

    2013-01-01

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

  13. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.

    Science.gov (United States)

    Zhang, Kun; Huang, Biwei; Zhang, Jiji; Glymour, Clark; Schölkopf, Bernhard

    2017-08-01

    It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets (the data sets may have different experimental conditions or data collection conditions). Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

  14. Causal Univariate Spatial-Temporal Autoregressive Moving Averages (STARMA) Modelling of Target Information to Generate Tasking of a World-Wide Sensor System

    Science.gov (United States)

    1992-03-01

    Objective .. .. .. .. ... ... ... ... ... ... ... ... 3 II. Literature Search and Review . .. .. .. .. ... ... ... ... ... ... ... ... 4 2.1 Introduction...Literature Search and Review 2. I Introduction The following paragraphs will summarize literature pertinent to this research. The literature summary...Strawberry," Phytopathology , 78: 246-252 (1988). 23. Stoffer, David S. "Estimation and Identification of Space-Time ARMAX Models in the Pres- ence of Missing

  15. Directed information graphs for the Granger causality of multivariate time series

    Science.gov (United States)

    Gao, Wei; Cui, Wanqi; Ye, Wenna

    2017-11-01

    In this paper, we investigate the links between (strong) Granger causality and directed information theory for multivariate time series. Based on the decomposition of conditional directed information, we propose a definition of Granger causality including instantaneous variables in the conditional set, which can avoid the spurious causality. The directed information graphs are presented to describe the Granger causality and instantaneous coupling. The structure learning of the graph models is based on the Leonenko's k-nn estimator of the statistics and a permutation test of the significant. Finally, we demonstrate the numerical implementation of these techniques on linear and nonlinear time series.

  16. The why of things: causality in science, medicine, and life

    CERN Document Server

    Rabins, Peter V.

    2013-01-01

    Why was there a meltdown at the Fukushima power plant? Why do some people get cancer and not others? Why is global warming happening? Why does one person get depressed in the face of life's vicissitudes while another finds resilience? Questions like these -- questions of causality -- form the basis of modern scientific inquiry, posing profound intellectual and methodological challenges for researchers in the physical, natural, biomedical, and social sciences. In this groundbreaking book, noted psychiatrist and author Peter Rabins offers a conceptual framework for analyzing daunting questions of causality. Navigating a lively intellectual voyage between the shoals of strict reductionism and relativism, Rabins maps a three-facet model of causality and applies it to a variety of questions in science, medicine, economics, and more. Throughout this book, Rabins situates his argument within relevant scientific contexts, such as quantum mechanics, cybernetics, chaos theory, and epigenetics. A renowned communicator o...

  17. Momentary and integrative response strategies in causal judgment.

    Science.gov (United States)

    Collins, Darrell J; Shanks, David R

    2002-10-01

    Associative models of causal learning predict recency effects. Judgments at the end of a trial series should be strongly biased by recently presented information. Prior research, however, presents a contrasting picture of human performance. López, Shanks, Almaraz, and Fernández (1998) observed recency, whereas Dennis and Ahn (2001) found the opposite, primacy. Here we replicate both of these effects and provide an explanation for this paradox. Four experiments show that the effect of trial order on judgments is a function of judgment frequency, where incremental judgments lead to recency while single final judgments abolish recency and lead instead to integration of information across trials (i.e., primacy). These results challenge almost all existing accounts of causal judgment. We propose a modified associative account in which participants can base their causal judgments either on current associative strength (momentary strategy) or on the cumulative change in associative strength since the previous judgment (integrative strategy).

  18. Tensor products of process matrices with indefinite causal structure

    Science.gov (United States)

    Jia, Ding; Sakharwade, Nitica

    2018-03-01

    Theories with indefinite causal structure have been studied from both the fundamental perspective of quantum gravity and the practical perspective of information processing. In this paper we point out a restriction in forming tensor products of objects with indefinite causal structure in certain models: there exist both classical and quantum objects the tensor products of which violate the normalization condition of probabilities, if all local operations are allowed. We obtain a necessary and sufficient condition for when such unrestricted tensor products of multipartite objects are (in)valid. This poses a challenge to extending communication theory to indefinite causal structures, as the tensor product is the fundamental ingredient in the asymptotic setting of communication theory. We discuss a few options to evade this issue. In particular, we show that the sequential asymptotic setting does not suffer the violation of normalization.

  19. On the non-causal link between volatility and growth

    DEFF Research Database (Denmark)

    Posch, Olaf; Wälde, Klaus

    A model highlighting the endogeneity of both volatility and growth is presented. Volatility and growth are therefore correlated but there is no causal link from volatility to growth. This joint endogeneity is illustrated by working out the effects through which economies with different tax levels...... in the conditional variance equation. Otherwise an omitted variable bias is likely....

  20. Causal Reasoning in Medicine: Analysis of a Protocol.

    Science.gov (United States)

    Kuipers, Benjamin; Kassirer, Jerome P.

    1984-01-01

    Describes the construction of a knowledge representation from the identification of the problem (nephrotic syndrome) to a running computer simulation of causal reasoning to provide a vertical slice of the construction of a cognitive model. Interactions between textbook knowledge, observations of human experts, and computational requirements are…

  1. Electromagnetic pulses, localized and causal

    Science.gov (United States)

    Lekner, John

    2018-01-01

    We show that pulse solutions of the wave equation can be expressed as time Fourier superpositions of scalar monochromatic beam wave functions (solutions of the Helmholtz equation). This formulation is shown to be equivalent to Bateman's integral expression for solutions of the wave equation, for axially symmetric solutions. A closed-form one-parameter solution of the wave equation, containing no backward-propagating parts, is constructed from a beam which is the tight-focus limit of two families of beams. Application is made to transverse electric and transverse magnetic pulses, with evaluation of the energy, momentum and angular momentum for a pulse based on the general localized and causal form. Such pulses can be represented as superpositions of photons. Explicit total energy and total momentum values are given for the one-parameter closed-form pulse.

  2. Bayesian detection of causal rare variants under posterior consistency.

    Directory of Open Access Journals (Sweden)

    Faming Liang

    Full Text Available Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD, to tackle this problem. The new method simultaneously addresses two issues: (i (Global association test Are there any of the variants associated with the disease, and (ii (Causal variant detection Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

  3. Bayesian detection of causal rare variants under posterior consistency.

    KAUST Repository

    Liang, Faming

    2013-07-26

    Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

  4. Space-time as a causal set

    International Nuclear Information System (INIS)

    Bombelli, L.; Lee, J.; Meyer, D.; Sorkin, R.D.

    1987-01-01

    We propose that space-time at the smallest scales is in reality a causal set: a locally finite set of elements endowed with a partial order corresponding to the macroscopic relation that defines past and future. We explore how a Lorentzian manifold can approximate a causal set, noting in particular that the thereby defined effective dimensionality of a given causal set can vary with length scale. Finally, we speculate briefly on the quantum dynamics of causal sets, indicating why an appropriate choice of action can reproduce general relativity in the classical limit

  5. Does causal action facilitate causal perception in infants younger than 6 months of age?

    Science.gov (United States)

    Rakison, David H; Krogh, Lauren

    2012-01-01

    Previous research has established that infants are unable to perceive causality until 6¼ months of age. The current experiments examined whether infants' ability to engage in causal action could facilitate causal perception prior to this age. In Experiment 1, 4½-month-olds were randomly assigned to engage in causal action experience via Velcro sticky mittens or not engage in causal action because they wore non-sticky mittens. Both groups were then tested in the visual habituation paradigm to assess their causal perception. Infants who engaged in causal action - but not those without this causal action experience - perceived the habituation events as causal. Experiment 2 used a similar design to establish that 4½-month-olds are unable to generalize their own causal action to causality observed in dissimilar objects. These data are the first to demonstrate that infants under 6 months of age can perceive causality, and have implications for the mechanisms underlying the development of causal perception. © 2011 Blackwell Publishing Ltd.

  6. A marginal structural model to estimate the causal effect of antidepressant medication treatment on viral suppression among homeless and marginally housed persons with HIV.

    Science.gov (United States)

    Tsai, Alexander C; Weiser, Sheri D; Petersen, Maya L; Ragland, Kathleen; Kushel, Margot B; Bangsberg, David R

    2010-12-01

    Depression strongly predicts nonadherence to human immunodeficiency virus (HIV) antiretroviral therapy, and adherence is essential to maintaining viral suppression. This suggests that pharmacologic treatment of depression may improve virologic outcomes. However, previous longitudinal observational analyses have inadequately adjusted for time-varying confounding by depression severity, which could yield biased estimates of treatment effect. Application of marginal structural modeling to longitudinal observation data can, under certain assumptions, approximate the findings of a randomized controlled trial. To determine whether antidepressant medication treatment increases the probability of HIV viral suppression. Community-based prospective cohort study with assessments conducted every 3 months. Community-based research field site in San Francisco, California. One hundred fifty-eight homeless and marginally housed persons with HIV who met baseline immunologic (CD4+ T-lymphocyte count, 13) inclusion criteria, observed from April 2002 through August 2007. Probability of achieving viral suppression to less than 50 copies/mL. Secondary outcomes of interest were probability of being on an antiretroviral therapy regimen, 7-day self-reported percentage adherence to antiretroviral therapy, and probability of reporting complete (100%) adherence. Marginal structural models estimated a 2.03 greater odds of achieving viral suppression (95% confidence interval [CI], 1.15-3.58; P = .02) resulting from antidepressant medication treatment. In addition, antidepressant medication use increased the probability of antiretroviral uptake (weighted odds ratio, 3.87; 95% CI, 1.98-7.58; P effect is likely attributable to improved adherence to a continuum of HIV care, including increased uptake and adherence to antiretroviral therapy.

  7. Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

    International Nuclear Information System (INIS)

    Neelamkavil, Raphael

    2014-01-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

  8. Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

    Energy Technology Data Exchange (ETDEWEB)

    Neelamkavil, Raphael

    2014-07-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

  9. The mutual causality analysis between the stock and futures markets

    Science.gov (United States)

    Yao, Can-Zhong; Lin, Qing-Wen

    2017-07-01

    In this paper we employ the conditional Granger causality model to estimate the information flow, and find that the improved model outperforms the Granger causality model in revealing the asymmetric correlation between stocks and futures in the Chinese market. First, we find that information flows estimated by Granger causality tests from futures to stocks are greater than those from stocks to futures. Additionally, average correlation coefficients capture some important characteristics between stock prices and information flows over time. Further, we find that direct information flows estimated by conditional Granger causality tests from stocks to futures are greater than those from futures to stocks. Besides, the substantial increases of information flows and direct information flows exhibit a certain degree of synchronism with the occurrences of important events. Finally, the comparative analysis with the asymmetric ratio and the bootstrap technique demonstrates the slight asymmetry of information flows and the significant asymmetry of direct information flows. It reveals that the information flows from futures to stocks are slightly greater than those in the reverse direction, while the direct information flows from stocks to futures are significantly greater than those in the reverse direction.

  10. Verification of Causal Influences of Reasoning Skills and Epistemology on Physics Conceptual Learning

    Science.gov (United States)

    Ding, Lin

    2014-01-01

    This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills…

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

    Directory of Open Access Journals (Sweden)

    Nebiye Yamak

    2016-06-01

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

  12. Quantum field theory and gravity in causal sets

    Science.gov (United States)

    Sverdlov, Roman M.

    Causal set is a model of space time that allows to reconcile discreteness and manifest relativistic invariance. This is done by viewing space time as finite, partially ordered set. The elements of the set are viewed as points of space time, or events; the partial ordering between them is viewed as causal relations. It has been shown that, in discrete scenario, the information about causal relations between events can, indeed, approximate the metric. The goal of this thesis is to introduce matter fields and their Lagrangians into causal set context. This is a two step process. The first step is to re-define gauge fields, gravity, and distances in such a way that no reference to Lorentz index is made. This is done by defining gauge fields as two-point real valued functions, and gravitational field as causal structure itself. Once the above is done, Lagrangians have to be defined in a way that they don't refer to Lorentzian indices either. This is done by introducing a notion of type 1 and type 2 Lagrangian generators, coupled with respective machinery that "translates" each generator into corresponding Lagrangian. The fields that are subject to these generators are, respectively, defined as type 1 and type 2. The main difference between two kinds of fields is the prediction of different behavior in different dimensions of type 2 fields. However, despite our inability to travel to different dimensions, gravity was shown to be type 2 based on the erroneous predictions of its 4-dimensional behavior if it was viewed as type 1. However, no erroneous predictions are made if non-gravitational fields are viewed as either type 1 or type 2, thus the nature of the latter is still an open question. Finally, an attempt was made to provide interpretation of quantum mechanics that would allow to limit fluctuations of causal structure to allow some topological background. However, due to its controversial nature, it is placed in the Appendix.

  13. Timing and causality in the generation of learned eyelid responses

    Directory of Open Access Journals (Sweden)

    Raudel eSánchez-Campusano

    2011-08-01

    Full Text Available The cerebellum-red nucleus-facial motoneuron (Mn pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP neurons and orbicularis oculi (OO Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing to longer-lasting ranges (interval timing expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and premotor spike-timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar-Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IP neurons may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality than to their generation and/or initiation.

  14. An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

    KAUST Repository

    Zenil, Hector

    2017-09-08

    We introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system\\'s actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system\\'s dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system\\'s intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.

  15. Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

    Science.gov (United States)

    Wang, Zhe; Alahmadi, Ahmed; Zhu, David C; Li, Tongtong

    2016-05-01

    This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

  16. Quasi-Experimental Designs for Causal Inference

    Science.gov (United States)

    Kim, Yongnam; Steiner, Peter

    2016-01-01

    When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

  17. Special Relativity, Causality and Quantum Mechanics-2

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 11; Issue 9. Special Relativity, Causality and Quantum Mechanics - 2. Guruprasad Kar Samir Kunkri Sujit K Choudhary. General Article Volume 11 Issue 9 ... Keywords. Causality; quantum entanglement; cloning; local realism; completely positive maps.

  18. Causal Mediation Analysis: Warning! Assumptions Ahead

    Science.gov (United States)

    Keele, Luke

    2015-01-01

    In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

  19. Heterogeneous Causal Effects and Sample Selection Bias

    DEFF Research Database (Denmark)

    Breen, Richard; Choi, Seongsoo; Holm, Anders

    2015-01-01

    The role of education in the process of socioeconomic attainment is a topic of long standing interest to sociologists and economists. Recently there has been growing interest not only in estimating the average causal effect of education on outcomes such as earnings, but also in estimating how...... causal effects might vary over individuals or groups. In this paper we point out one of the under-appreciated hazards of seeking to estimate heterogeneous causal effects: conventional selection bias (that is, selection on baseline differences) can easily be mistaken for heterogeneity of causal effects....... This might lead us to find heterogeneous effects when the true effect is homogenous, or to wrongly estimate not only the magnitude but also the sign of heterogeneous effects. We apply a test for the robustness of heterogeneous causal effects in the face of varying degrees and patterns of selection bias...

  20. Repair of Partly Misspecified Causal Diagrams.

    Science.gov (United States)

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

    2017-07-01

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

  1. On the conceptual distinction of general causality orientations

    DEFF Research Database (Denmark)

    Olesen, Martin Hammershøj

    electronic questionnaires of dispositional personality traits (NEO-FFI) and general causality orientations (GCOS). Proposed separate latent models and alternative shared latent models of the underlying individual differences constructs had been developed in a previous exploratory study (Olesen, Thomsen......, Schnieber & Tønnesvang, under review). The models were tested using confirmatory factor analyses. Results showed that all three causality orientations were distinct from but closely related to personality traits. Hence, integrative efforts are suggested in relation to comprehensive personality frameworks......This study investigates the conceptual overlap and distinction between individual differences in the Five-Factor Model and Self-determination theory. Participants were 223 adults (age mean=43.74; 60.09% women), who originated in a Danish national probability sample. The participants completed...

  2. Causal ubiquity in quantum physics a superluminal and local-causal physical ontology

    CERN Document Server

    Neelamkavil, Raphael

    2014-01-01

    A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly non-causal processes, something exists processually in extension-motion, between the causal and the non-causal. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That

  3. Trygve Haavelmo and the Emergence of Causal Calculus

    Science.gov (United States)

    2014-06-01

    causal models and their tentative conclusions, owes its renaissance to Haavelmo’s third insight—a concrete procedure for eliciting answers to policy...in terms of the model’s parameters but in terms of a procedure (or “ surgery ”) that hypothetically modifies the structure of the model so as to simulate...interprets the counterfactual as an incisive “ surgery ” that suppresses all mechanisms that may contribute to variations in X and imposes the equality X = x

  4. Causal systems categories: differences in novice and expert categorization of causal phenomena.

    Science.gov (United States)

    Rottman, Benjamin M; Gentner, Dedre; Goldwater, Micah B

    2012-07-01

    We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures. Copyright © 2012 Cognitive Science Society, Inc.

  5. There aren't plenty more fish in the sea: a causal network approach.

    Science.gov (United States)

    Nikolic, Milena; Lagnado, David A

    2015-11-01

    The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two-way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology. © 2015 The British Psychological Society.

  6. Psychiatric comorbidity and causal disease models

    NARCIS (Netherlands)

    van Loo, Hanna M.; Romeijn, Jan-Willem; de Jonge, Peter; Schoevers, Robert A.

    2013-01-01

    In psychiatry, comorbidity is the rule rather than the exception. Up to 45% of all patients are classified as having more than one psychiatric disorder. These high rates of comorbidity have led to a debate concerning the interpretation of this phenomenon. Some authors emphasize the problematic

  7. On Martingales, Causality, Identifiability and Model Selection

    DEFF Research Database (Denmark)

    Sokol, Alexander

    or not. We attempt to elucidate what happens in the case where the error distributions are close to but not exactly Gaussian. Finally, Chapter 10 discusses degrees of freedom in nonlinear regression. Our motivating problem is that of L1-constrained and L1-penalized estimation in nonlinear regression. Our......Ornstein-Uhlenbeck SDEs, where explicit calculations may be made for the postintervention distributions. Chapter 9 concerns identiability of the mixing matrix in ICA. It is a well-known result that identiability of the mixing matrix depends crucially on whether the error distributions are Gaussian...

  8. A Simple Test for Causality in Volatility

    Directory of Open Access Journals (Sweden)

    Chia-Lin Chang

    2017-03-01

    Full Text Available An early development in testing for causality (technically, Granger non-causality in the conditional variance (or volatility associated with financial returns was the portmanteau statistic for non-causality in the variance of Cheng and Ng (1996. A subsequent development was the Lagrange Multiplier (LM test of non-causality in the conditional variance by Hafner and Herwartz (2006, who provided simulation results to show that their LM test was more powerful than the portmanteau statistic for sample sizes of 1000 and 4000 observations. While the LM test for causality proposed by Hafner and Herwartz (2006 is an interesting and useful development, it is nonetheless arbitrary. In particular, the specification on which the LM test is based does not rely on an underlying stochastic process, so the alternative hypothesis is also arbitrary, which can affect the power of the test. The purpose of the paper is to derive a simple test for causality in volatility that provides regularity conditions arising from the underlying stochastic process, namely a random coefficient autoregressive process, and a test for which the (quasi- maximum likelihood estimates have valid asymptotic properties under the null hypothesis of non-causality. The simple test is intuitively appealing as it is based on an underlying stochastic process, is sympathetic to Granger’s (1969, 1988 notion of time series predictability, is easy to implement, and has a regularity condition that is not available in the LM test.

  9. Causal inference, probability theory, and graphical insights.

    Science.gov (United States)

    Baker, Stuart G

    2013-11-10

    Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M-bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK-Plot for understanding Simpson's paradox with a binary confounder, the BK2-Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD-Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.

  10. On the origin of Hill's causal criteria.

    Science.gov (United States)

    Morabia, A

    1991-09-01

    The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

  11. Causality and Time in Historical Institutionalism

    DEFF Research Database (Denmark)

    Mahoney, James; Mohamedali, Khairunnisa; Nguyen, Christoph

    2016-01-01

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

  12. Dual Causality and the Autonomy of Biology.

    Science.gov (United States)

    Bock, Walter J

    2017-03-01

    Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

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

    Science.gov (United States)

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

    2017-05-01

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

  14. The continuum limit of causal fermion systems from Planck scale structures to macroscopic physics

    CERN Document Server

    Finster, Felix

    2016-01-01

    This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries". The dynamics is described by a novel variational principle, called the causal action principle. In addition to the basics, the book provides all the necessary mathematical background and explains how the causal action principle gives rise to the interactions of the standard model plus gravity on the level of second-quantized fermionic fields coupled to classical bosonic fields. The focus is on getting a mathematically sound connection between causal fermion systems and physical systems in Minkowski space. The book is intended for graduate students e...

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  16. Renewable Energy Consumption and Economic Growth in Nine OECD Countries: Bounds Test Approach and Causality Analysis

    Science.gov (United States)

    Hung-Pin, Lin

    2014-01-01

    The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries—United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain. PMID:24558343

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

    Science.gov (United States)

    Hung-Pin, Lin

    2014-01-01

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

  18. Normalizability analysis of the generalized quantum electrodynamics from the causal point of view

    Science.gov (United States)

    Bufalo, R.; Pimentel, B. M.; Soto, D. E.

    2017-09-01

    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 analyze the implication of the gauge invariance onto the model and obtain the respective Ward-Takahashi-Fradkin identities.

  19. Selecting appropriate cases when tracing causal mechanisms

    DEFF Research Database (Denmark)

    Beach, Derek; Pedersen, Rasmus Brun

    2016-01-01

    The last decade has witnessed resurgence in the interest in studying the causal mechanisms linking causes and outcomes in the social sciences. This article explores the overlooked implications for case selection when tracing mechanisms using in-depth case studies. Our argument is that existing case...... selection guidelines are appropriate for research aimed at making cross-case claims about causal relationships, where case selection is primarily used to control for other causes. However, existing guidelines are not in alignment with case-based research that aims to trace mechanisms, where the goal...... is to unpack the causal mechanism between X and Y, enabling causal inferences to be made because empirical evidence is provided for how the mechanism actually operated in a particular case. The in-depth, within-case tracing of how mechanisms operate in particular cases produces what can be termed mechanistic...

  20. Causality Between Urban Concentration and Environmental Quality

    Directory of Open Access Journals (Sweden)

    Amin Pujiati

    2015-08-01

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

  1. Risk and causality in newspaper reporting.

    Science.gov (United States)

    Boholm, Max

    2009-11-01

    The study addresses the textual representation of risk and causality in news media reporting. The analytical framework combines two theoretical perspectives: media frame analysis and the philosophy of causality. Empirical data derive from selected newspaper articles on risks in the Göta älv river valley in southwest Sweden from 1994 to 2007. News media content was coded and analyzed with respect to causal explanations of risk issues. At the level of individual articles, this study finds that the media provide simple causal explanations of risks such as water pollution, landslides, and flooding. Furthermore, these explanations are constructed, or framed, in various ways, the same risk being attributed to different causes in different articles. However, the study demonstrates that a fairly complex picture of risks in the media emerges when extensive material is analyzed systematically.

  2. Rate-Agnostic (Causal) Structure Learning.

    Science.gov (United States)

    Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

    2015-12-01

    Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.

  3. Causales de ausencia de responsabilidad penal

    Directory of Open Access Journals (Sweden)

    Jaime Sandoval Fernández

    2003-01-01

    Full Text Available Este trabajo se ocupa de las causales de ausencia de responsabilidad penal, especialmente de aquellas que tienen efecto en el injusto. Como subtemas se delimita el concepto de responsabilidad penal y su ausencia. Se estudian las principales teorias a cerca de la relación tipicidad-antijuridicidad y su incidencia en el derecho penal colombiano. Por último contiene una propuesta acerca de cómo deberian agruparse las causales del arto 32 C. PlOO.

  4. Kant on causal laws and powers.

    Science.gov (United States)

    Henschen, Tobias

    2014-12-01

    The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets.

  5. Nonparametric causal effects based on incremental propensity score interventions

    OpenAIRE

    Kennedy, Edward H.

    2017-01-01

    Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to non-identification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental ...

  6. Oil prices and the world business cycle: A causal investigation

    OpenAIRE

    Tapia, Jose

    2016-01-01

    Oil shocks have been often considered as exogenous factors responsible of economic downturns. In this paper the hypothesized exogeneity of oil prices is investigated by using running cross-correlations, distributed lag-regressions, Granger causality tests and VAR models applied to annual data 1960-2014 of oil prices and global economic activity—as measured by world GDP. Strong evidence is found that (a) the relation between oil prices and the global economy has significantly changed since th...

  7. Reconstructing Causal Biological Networks through Active Learning.

    Directory of Open Access Journals (Sweden)

    Hyunghoon Cho

    Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

  8. From causal dynamical triangulations to astronomical observations

    Science.gov (United States)

    Mielczarek, Jakub

    2017-09-01

    This letter discusses phenomenological aspects of dimensional reduction predicted by the Causal Dynamical Triangulations (CDT) approach to quantum gravity. The deformed form of the dispersion relation for the fields defined on the CDT space-time is reconstructed. Using the Fermi satellite observations of the GRB 090510 source we find that the energy scale of the dimensional reduction is E* > 0.7 \\sqrt{4-d\\text{UV}} \\cdot 1010 \\text{GeV} at (95% CL), where d\\text{UV} is the value of the spectral dimension in the UV limit. By applying the deformed dispersion relation to the cosmological perturbations it is shown that, for a scenario when the primordial perturbations are formed in the UV region, the scalar power spectrum PS \\propto kn_S-1 , where n_S-1≈ \\frac{3 r (d\\text{UV}-2)}{(d\\text{UV}-1)r-48} . Here, r is the tensor-to-scalar ratio. We find that within the considered model, the predicted from CDT deviation from the scale invariance (n_S=1) is in contradiction with the up to date Planck and BICEP2.

  9. Illness causal beliefs in Turkish immigrants.

    Science.gov (United States)

    Minas, Harry; Klimidis, Steven; Tuncer, Can

    2007-07-24

    People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different

  10. Normative and descriptive accounts of the influence of power and contingency on causal judgement.

    Science.gov (United States)

    Perales, José C; Shanks, David R

    2003-08-01

    The power PC theory (Cheng, 1997) is a normative account of causal inference, which predicts that causal judgements are based on the power p of a potential cause, where p is the cause-effect contingency normalized by the base rate of the effect. In three experiments we demonstrate that both cause-effect contingency and effect base-rate independently affect estimates in causal learning tasks. In Experiment 1, causal strength judgements were directly related to power p in a task in which the effect base-rate was manipulated across two positive and two negative contingency conditions. In Experiments 2 and 3 contingency manipulations affected causal estimates in several situations in which power p was held constant, contrary to the power PC theory's predictions. This latter effect cannot be explained by participants' conflation of reliability and causal strength, as Experiment 3 demonstrated independence of causal judgements and confidence. From a descriptive point of view, the data are compatible with Pearce's (1987) model, as well as with several other judgement rules, but not with the Rescorla-Wagner (Rescorla & Wagner, 1972) or power PC models.

  11. Entanglement entropy in causal set theory

    Science.gov (United States)

    Sorkin, Rafael D.; Yazdi, Yasaman K.

    2018-04-01

    Entanglement entropy is now widely accepted as having deep connections with quantum gravity. It is therefore desirable to understand it in the context of causal sets, especially since they provide in a natural manner the UV cutoff needed to render entanglement entropy finite. Formulating a notion of entanglement entropy in a causal set is not straightforward because the type of canonical hypersurface-data on which its definition typically relies is not available. Instead, we appeal to the more global expression given in Sorkin (2012 (arXiv:1205.2953)) which, for a Gaussian scalar field, expresses the entropy of a spacetime region in terms of the field’s correlation function within that region (its ‘Wightman function’ W(x, x') ). Carrying this formula over to the causal set, one obtains an entropy which is both finite and of a Lorentz invariant nature. We evaluate this global entropy-expression numerically for certain regions (primarily order-intervals or ‘causal diamonds’) within causal sets of 1  +  1 dimensions. For the causal-set counterpart of the entanglement entropy, we obtain, in the first instance, a result that follows a (spacetime) volume law instead of the expected (spatial) area law. We find, however, that one obtains an area law if one truncates the commutator function (‘Pauli–Jordan operator’) and the Wightman function by projecting out the eigenmodes of the Pauli–Jordan operator whose eigenvalues are too close to zero according to a geometrical criterion which we describe more fully below. In connection with these results and the questions they raise, we also study the ‘entropy of coarse-graining’ generated by thinning out the causal set, and we compare it with what one obtains by similarly thinning out a chain of harmonic oscillators, finding the same, ‘universal’ behaviour in both cases.

  12. Causal attribution of mental illness in South-Eastern Nigeria.

    Science.gov (United States)

    Ikwuka, Ugo; Galbraith, Niall; Nyatanga, Lovemore

    2014-05-01

    Understanding of mental illness in sub-Saharan Africa has remained under-researched in spite of the high and increasing neuropsychiatric burden of disease in the region. This study investigated the causal beliefs that the Igbo people of south-eastern Nigeria hold about schizophrenia, with a view to establishing the extent to which the population makes psychosocial, biological and supernatural attributions. Multi-stage sampling was used to select participants (N = 200) to which questionnaires were administered. Mean comparison of the three causal models revealed a significant endorsement of supernatural causation. Logistic regressions revealed significant contributions of old age and female gender to supernatural attribution; old age, high education and Catholic religious denomination to psychosocial attributions; and high education to biological attributions. It is hoped that the findings would enlighten, augment literature and enhance mental health care service delivery.

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

    International Nuclear Information System (INIS)

    Lean, Hooi Hooi; Smyth, Russell

    2010-01-01

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

  14. The Use of Causal Mapping in the Design of Sustainability Performance Measurement Systems

    DEFF Research Database (Denmark)

    Parisi, Cristiana

    2013-01-01

    , in order to capture managerial cognition and derive a model that reflects companies’ competitive advantages. The resulting causal map is a prerequisite and serves as a building block for the design of the organisation’s performance management systems for sustainability. This study relies on qualitative...... organisations’ strategic performance measurement systems (SPMSs). This study’s main contribution is the triangulation of multiple qualitative methods to enhance the reliability of causal maps. This innovative approach supports the use of causal mapping to extract managerial tacit knowledge in order to identify...

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

    Science.gov (United States)

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

    2013-08-28

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

  16. Preschoolers prefer to learn causal information

    Directory of Open Access Journals (Sweden)

    Aubry eAlvarez

    2015-02-01

    Full Text Available Young children, in general, appear to have a strong drive to explore the environment in ways that reveal its underlying causal structure. But are they really attuned specifically to casual information in this quest for understanding, or do they show equal interest in other types of non-obvious information about the world? To answer this question, we introduced 20 three-year-old children to two puppets who were anxious to tell the child about a set of novel artifacts and animals. One puppet consistently described causal properties of the items while the other puppet consistently described carefully matched non-causal properties of the same items. After a familiarization period in which children learned which type of information to expect from each informant, children were given the opportunity to choose which they wanted to hear describe each of eight pictured test items. On average, children chose to hear from the informant that provided causal descriptions on 72% of the trials. This preference for causal information has important implications for explaining the role of conceptual information in supporting early learning and may suggest means for maximizing interest and motivation in young children.

  17. The Relevance of Causal Social Construction

    Directory of Open Access Journals (Sweden)

    Marques Teresa

    2017-02-01

    Full Text Available Social constructionist claims are surprising and interesting when they entail that presumably natural kinds are in fact socially constructed. The claims are interesting because of their theoretical and political importance. Authors like Díaz-León argue that constitutive social construction is more relevant for achieving social justice than causal social construction. This paper challenges this claim. Assuming there are socially salient groups that are discriminated against, the paper presents a dilemma: if there were no constitutively constructed social kinds, the causes of the discrimination of existing social groups would have to be addressed, and understanding causal social construction would be relevant to achieve social justice. On the other hand, not all possible constitutively socially constructed kinds are actual social kinds. If an existing social group is constitutively constructed as a social kind K, the fact that it actually exists as a K has social causes. Again, causal social construction is relevant. The paper argues that (i for any actual social kind X, if X is constitutively socially constructed as K, then it is also causally socially constructed; and (ii causal social construction is at least as relevant as constitutive social construction for concerns of social justice. For illustration, I draw upon two phenomena that are presumed to contribute towards the discrimination of women: (i the poor performance effects of stereotype threat, and (ii the silencing effects of gendered language use.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  19. Breaking the arrows of causality

    DEFF Research Database (Denmark)

    Valsiner, Jaan

    2014-01-01

    role of cyclical models in biology has made it possible to move from the idea of genetic determination to that of epigenetic negotiation as the core of biological theory. In psychology, catalytic thinking has been outside of the realm of accepted scientific schemes, as the axiomatic dependence upon...

  20. Causal binding of actions to their effects.

    Science.gov (United States)

    Buehner, Marc J; Humphreys, Gruffydd R

    2009-10-01

    According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.

  1. Causal inheritance in plane wave quotients

    International Nuclear Information System (INIS)

    Hubeny, Veronika E.; Rangamani, Mukund; Ross, Simon F.

    2003-01-01

    We investigate the appearance of closed timelike curves in quotients of plane waves along spacelike isometries. First we formulate a necessary and sufficient condition for a quotient of a general spacetime to preserve stable causality. We explicitly show that the plane waves are stably causal; in passing, we observe that some pp-waves are not even distinguishing. We then consider the classification of all quotients of the maximally supersymmetric ten-dimensional plane wave under a spacelike isometry, and show that the quotient will lead to closed timelike curves iff the isometry involves a translation along the u direction. The appearance of these closed timelike curves is thus connected to the special properties of the light cones in plane wave spacetimes. We show that all other quotients preserve stable causality

  2. The power of possibility: causal learning, counterfactual reasoning, and pretend play.

    Science.gov (United States)

    Buchsbaum, Daphna; Bridgers, Sophie; Skolnick Weisberg, Deena; Gopnik, Alison

    2012-08-05

    We argue for a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms, specifically the use of causal models and Bayesian learning. We suggest that exploratory childhood learning, childhood play in particular, and causal cognition are closely connected. We report an empirical study demonstrating one such connection--a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretence were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that, during human evolution, computations that were initially reserved for solving particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.

  3. Morse theory on timelike and causal curves

    International Nuclear Information System (INIS)

    Everson, J.; Talbot, C.J.

    1976-01-01

    It is shown that the set of timelike curves in a globally hyperbolic space-time manifold can be given the structure of a Hilbert manifold under a suitable definition of 'timelike.' The causal curves are the topological closure of this manifold. The Lorentzian energy (corresponding to Milnor's energy, except that the Lorentzian inner product is used) is shown to be a Morse function for the space of causal curves. A fixed end point index theorem is obtained in which a lower bound for the index of the Hessian of the Lorentzian energy is given in terms of the sum of the orders of the conjugate points between the end points. (author)

  4. The Causal Relationship between Corruption and Poverty: A Panel Data Analysis

    OpenAIRE

    Negin, Vahideh; Abd Rashid, Zakariah; Nikopour, Hesam

    2010-01-01

    Most of the studies which have investigated the link between corruption and poverty may draw conclusions on causality in the form of models that only show correlation. This study is set out to investigate the Granger causal relationship between corruption and poverty. It uses dynamic panel system GMM estimators, focuses on capability poverty using human poverty index (HPI) and is based on a sample of 97 developing countries during 1997-2006. The empirical findings reveal that corruption and p...

  5. Causal ordering of academic self-concept and achievement: effects of type of achievement measure.

    Science.gov (United States)

    Pinxten, Maarten; De Fraine, Bieke; Van Damme, Jan; D'Haenens, Ellen

    2010-12-01

    The relation between academic self-concept and achievement has been examined in a large number of studies. The majority of these studies have found evidence for a reciprocal effects model. However, there is an ongoing debate on how students' achievement should be measured and whether the type of achievement indicator (grades, tests, teacher ratings) affects the causal pattern found in these studies. The study aims at clarifying how the types of achievement measures and the way they are modelled can affect the results of causal ordering studies. In that sense, the study will yield recommendations for researchers in this domain and also provide some direction for practitioners seeking ways to enhance their students' achievement and/or academic self-concept. Repeated measures of academic self-concept and achievement (standardized tests and teacher ratings) were examined in a sample of 1,753 students in Grades 7, 8, 10, and 12. Structural equation modelling was used. Several models (with different types and numbers of achievement measures) were compared. Only small differences were found between models using one or two indicators of achievement. All models generally supported the reciprocal effects model. However, the final model, wherein tests and teacher ratings were used as separate latent variables, showed different developmental patterns in the causal relation between academic self-concept and achievement. Researchers should interpret the results of causal ordering studies discerningly because the type of measure chosen as an indicator of achievement might affect the causal pattern between academic self-concept and achievement.

  6. BioCause: Annotating and analysing causality in the biomedical domain.

    Science.gov (United States)

    Mihăilă, Claudiu; Ohta, Tomoko; Pyysalo, Sampo; Ananiadou, Sophia

    2013-01-16

    Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining. We have defined an annotation scheme for enriching biomedical domain corpora with causality relations. This schema has subsequently been used to annotate 851 causal relations to form BioCause, a collection of 19 open-access full-text biomedical journal articles belonging to the subdomain of infectious diseases. These documents have been pre-annotated with named entity and event information in the context of previous shared tasks. We report an inter-annotator agreement rate of over 60% for triggers and of over 80% for arguments using an exact match constraint. These increase significantly using a relaxed match setting. Moreover, we analyse and describe the causality relations in BioCause from various points of view. This information can then be leveraged for the training of automatic causality detection systems. Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems. These will further influence the development of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new

  7. Accounting for occurrences: an explanation for some novel tendencies in causal judgment from contingency information.

    Science.gov (United States)

    White, Peter A

    2009-06-01

    Contingency information is information about empirical associations between possible causes and outcomes. In the present research, it is shown that, under some circumstances, there is a tendency for negative contingencies to lead to positive causal judgments and for positive contingencies to lead to negative causal judgments. If there is a high proportion of instances in which a candidate cause (CC) being judged is present, these tendencies are predicted by weighted averaging models of causal judgment. If the proportion of such instances is low, the predictions of weighted averaging models break down. It is argued that one of the main aims of causal judgment is to account for occurrences of the outcome. Thus, a CC is not given a high causal judgment if there are few or no occurrences of it, regardless of the objective contingency. This argument predicts that, if there is a low proportion of instances in which a CC is present, causal judgments are determined mainly by the number of Cell A instances (i.e., CC present, outcome occurs), and that this explains why weighted averaging models fail to predict judgmental tendencies under these circumstances. Experimental results support this argument.

  8. CausalTrail: Testing hypothesis using causal Bayesian networks [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Daniel Stöckel

    2015-12-01

    Full Text Available Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail

  9. Somatoform disorders and causal attributions in patients with suspected allergies: Do somatic causal attributions matter?

    Science.gov (United States)

    Groben, Sylvie; Hausteiner, Constanze

    2011-03-01

    Somatic causal illness attributions are being considered as potential positive criteria for somatoform disorders (SFDs) in DSM-V. The aim of this study was to investigate whether patients diagnosed with SFDs tend towards a predominantly somatic attribution style. We compared the causal illness attributions of 48 SFD and 149 non-somatoform disorder patients, in a sample of patients presenting for an allergy diagnostic work-up, and those of 47 controls hospitalised for allergen-specific venom immunotherapy. The SFD diagnosis was established by means of the Structured Clinical Interview for DSM-IV. Both spontaneous and prompted causal illness attributions were recorded through interview and by means of the causal dimension of the Revised Illness Perception Questionnaire (IPQ-R), respectively. Patients' spontaneous and prompted responses were assigned to a psychosocial, somatic, or mixed attribution style. Both in the free-response task and in their responses to the IPQ-R, SFD patients were no more likely than their nonsomatoform counterparts to focus on somatic explanations for their symptoms. They were just as likely to make psychosocial or mixed causal attributions. However, patients with SFDs were significantly more likely to find fault with medical care in the past. Our data do not support the use of somatic causal illness attributions as positive criteria for SFDs. They confirm the dynamic and multidimensional nature of causal illness attributions. Clinical implications of these findings are discussed. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Localization and causality in relativistic quantum mechanics

    International Nuclear Information System (INIS)

    Perez, J.F.; Wilde, I.F.

    It is shown that in relativistic quantum mechanics there is no criterion for the strict localization of a state in a bounded space-time region compatible with causality, translation covariance and the spectral condition (or positivity of energy together with Lorentz covariance) [pt

  11. Catastrophizing and Causal Beliefs in Whiplash

    NARCIS (Netherlands)

    Buitenhuis, J.; de Jong, P. J.; Jaspers, J. P. C.; Groothoff, J. W.

    2008-01-01

    Study Design. Prospective cohort study. Objective. This study investigates the role of pain catastrophizing and causal beliefs with regard to severity and persistence of neck complaints after motor vehicle accidents. Summary of Background Data. In previous research on low back pain, somatoform

  12. Special Relativity, Causality and Quantum Mechanics - 1

    Indian Academy of Sciences (India)

    information theory in general and quantum non-locality and entanglement in particular. Right. S Kunkri - current research interest is the role of entanglement in quantum information processing and the connection between quantum operations and causality. Centre. S K Choudhary - current research interest is the study of ...

  13. Marriage and Anomie: A Causal Argument

    Science.gov (United States)

    Lee, Gary R.

    1974-01-01

    A sample of 394 married couples is employed to test the possibility of an association between marital satisfaction and personal (attitudinal) anomie. The hypothesis is supported. Conclusions are offered relevant to anomie theory, and to utilization of marital and family phenomena as independent variables in causal explanations of nonfamily events.…

  14. Causal Meta-Analysis : Methodology and Applications

    NARCIS (Netherlands)

    Bax, L.J.

    2009-01-01

    Meta-analysis is a statistical method to summarize research data from multiple studies in a quantitative manner. This dissertation addresses a number of methodological topics in causal meta-analysis and reports the development and validation of meta-analysis software. In the first (methodological)

  15. Black Hole Complementarity and Violation of Causality

    OpenAIRE

    Rozenblit, Moshe

    2017-01-01

    Analysis of a massive shell collapsing on a solid sphere shows that black hole complementarity (BHC) violates causality in its effort to save information conservation. In particular, this note describes a hypothetical contraption based on BHC that would allow the transfer of information from the future to the present.

  16. THE CAUSAL TEXTURE OF TRADE UNION ENVIRONMENTS

    African Journals Online (AJOL)

    Admin

    This paper is an attempt to fill an important gap in the existing literature on trade unions by providing a more adequate theoretical formulation of trade union environments. The discussion suggests that unlike the environment of business and related organisations whose causal texture is understood in terms of uncertainty, ...

  17. Are bruxism and the bite causally related?

    NARCIS (Netherlands)

    Lobbezoo, F.; Ahlberg, J.; Manfredini, D.; Winocur, E.

    2012-01-01

    In the dental profession, the belief that bruxism and dental (mal-)occlusion (‘the bite’) are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query ‘Bruxism [Majr

  18. Sequential causal learning in humans and rats

    NARCIS (Netherlands)

    Lu, H.; Rojas, R.R.; Beckers, T.; Yuille, A.; Love, B.C.; McRae, K.; Sloutsky, V.M.

    2008-01-01

    Recent experiments (Beckers, De Houwer, Pineño, & Miller, 2005;Beckers, Miller, De Houwer, & Urushihara, 2006) have shown that pretraining with unrelated cues can dramatically influence the performance of humans in a causal learning paradigm and rats in a standard Pavlovian conditioning paradigm.

  19. Dimensional reduction in causal set gravity

    Science.gov (United States)

    Carlip, S.

    2015-12-01

    Results from a number of different approaches to quantum gravity suggest that the effective dimension of spacetime may drop to d = 2 at small scales. I show that two different dimensional estimators in causal set theory display the same behavior, and argue that a third, the spectral dimension, may exhibit a related phenomenon of ‘asymptotic silence.’

  20. Causal and Teleological Explanations in Biology

    Science.gov (United States)

    Yip, Cheng-Wai

    2009-01-01

    A causal explanation in biology focuses on the mechanism by which a biological process is brought about, whereas a teleological explanation considers the end result, in the context of the survival of the organism, as a reason for certain biological processes or structures. There is a tendency among students to offer a teleological explanation…

  1. Special Relativity, Causality and Quantum Mechanics - 2

    Indian Academy of Sciences (India)

    Peaceful Coexistence of Special Relativity and. Quantum Mechanics. As discussed in Part 1, in the framework of the special theory of relativity, causality holds. This can be stated as follows: there is a finite speed for any signal, i.e. , for anything that carries information, and the highest speed for any signal is identical to the ...

  2. Causal Relationship between Teachers' Job Performance and ...

    African Journals Online (AJOL)

    The study investigated teachers' job performance and students' academic achievement in secondary schools for the existence of bi-causal relationship in Nigeria. The ex-post facto research design was adopted in the study. The population of the study covered all the Economic teachers and senior school students in class ...

  3. Introducing mechanics by tapping core causal knowledge

    NARCIS (Netherlands)

    Klaassen, C.W.J.M.; Westra, A.S.; Emmett, K.M.; Eijkelhof, H.M.C.; Lijnse, P.L.

    2008-01-01

    This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force

  4. Causality and analyticity in quantum fields theory

    International Nuclear Information System (INIS)

    Iagolnitzer, D.

    1992-01-01

    This is a presentation of results on the causal and analytical structure of Green functions and on the collision amplitudes in fields theories, for massive particles of one type, with a positive mass and a zero spin value. (A.B.)

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

    African Journals Online (AJOL)

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

  6. The Causal Priority of Form in Aristotle

    Directory of Open Access Journals (Sweden)

    Kathrin Koslicki

    2014-12-01

    Full Text Available In various texts (e.g., Met. Z.17, Aristotle assigns priority to form, in its role as a principle and cause, over matter and the matter-form compound. Given the central role played by this claim in Aristotle's search for primary substance in the Metaphysics, it is important to understand what motivates him in locating the primary causal responsibility for a thing's being what it is with the form, rather than the matter. According to Met. Theta.8, actuality [energeia/entelecheia] in general is prior to potentiality [dunamis] in three ways, viz., in definition, time and substance. I propose an explicitly causal reading of this general priority claim, as it pertains to the matter-form relationship. The priority of form over matter in definition, time and substance, in my view, is best explained by appeal to the role of form as the formal, efficient and final cause of the matter-form compound, respectively, while the posteriority of matter to form according to all three notions of priority is most plausibly accounted for by the fact that the causal contribution of matter is limited to its role as material cause. When approached from this angle, the work of Met. Theta.8 can be seen to lend direct support to the more specific and explicitly causal priority claim we encounter in Met. Z.17, viz., that form is prior to matter in its role as the principle and primary cause of a matter-form compound's being what it is.

  7. Special Relativity, Causality and Quantum Mechanics - 1

    Indian Academy of Sciences (India)

    We discuss the significance of Einstein's second postulate of the special theory of relativity (STR) stipulating the constancy of the speed of light in vacuum. The causality that follows from the. STR may be a more general principle to orga- nize our knowledge of all phenomena. In partic- ular, quantum dynamics can be derived ...

  8. Special Relativity, Causality and Quantum Mechanics - 2

    Indian Academy of Sciences (India)

    tum world. An example of a game which can be won exploiting quantum entanglement, but which can never be won classically, is described. Peaceful Coexistence of Special Relativity and. Quantum Mechanics. As discussed in Part 1, in the framework of the special theory of relativity, causality holds. This can be stated.

  9. Probable autoimmune causal relationship between periodontitis and ...

    African Journals Online (AJOL)

    Periodontitis is a multifactorial disease with microbial dental plaque as the initiator of periodontal disease. However, the manifestation and progression of the disease is influenced by a wide variety of determinants and factors. The strongest type of causal relationship is the association of systemic and periodontal disease.

  10. Nuclear energy consumption and economic growth in OECD countries: Cross-sectionally dependent heterogeneous panel causality analysis

    International Nuclear Information System (INIS)

    Nazlioglu, Saban; Lebe, Fuat; Kayhan, Selim

    2011-01-01

    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.

  11. The role of causal maps in intellectual capital measurement and management

    DEFF Research Database (Denmark)

    Montemari, Marco; Nielsen, Christian

    2013-01-01

    model Findings – This paper illustrates how causal mapping can be used to understand how intellectual capital really works in the specific business context in which it is deployed. Moreover, exploiting the causal map as a platform for detracting a set of indicators can provide information on the length......Purpose – The purpose of this paper is to investigate the measurement and the management of the dynamic aspects of intellectual capital through the use of causal mapping. Design/methodology/approach – The study details the methods utilized in a single in-depth case study of a network-based business...... of the lag and the persistence of the effects of managerial actions. In addition, it can signal when and how to refine and update the causal map. The combination of these factors supports the dynamic measurement and management of intellectual capital. Research limitations/implications – The paper presented...

  12. 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.

  13. Uncertainty, causality and decision: The case of social risks and nuclear risk in particular

    International Nuclear Information System (INIS)

    Lahidji, R.

    2012-01-01

    Probability and causality are two indispensable tools for addressing situations of social risk. Causal relations are the foundation for building risk assessment models and identifying risk prevention, mitigation and compensation measures. Probability enables us to quantify risk assessments and to calibrate intervention measures. It therefore seems not only natural, but also necessary to make the role of causality and probability explicit in the definition of decision problems in situations of social risk. Such is the aim of this thesis.By reviewing the terminology of risk and the logic of public interventions in various fields of social risk, we gain a better understanding of the notion and of the issues that one faces when trying to model it. We further elaborate our analysis in the case of nuclear safety, examining in detail how methods and policies have been developed in this field and how they have evolved through time. This leads to a number of observations concerning risk and safety assessments.Generalising the concept of intervention in a Bayesian network allows us to develop a variety of causal Bayesian networks adapted to our needs. In this framework, we propose a definition of risk which seems to be relevant for a broad range of issues. We then offer simple applications of our model to specific aspects of the Fukushima accident and other nuclear safety problems. In addition to specific lessons, the analysis leads to the conclusion that a systematic approach for identifying uncertainties is needed in this area. When applied to decision theory, our tool evolves into a dynamic decision model in which acts cause consequences and are causally interconnected. The model provides a causal interpretation of Savage's conceptual framework, solves some of its paradoxes and clarifies certain aspects. It leads us to considering uncertainty with regard to a problem's causal structure as the source of ambiguity in decision-making, an interpretation which corresponds to a

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

    International Nuclear Information System (INIS)

    Kandemir Kocaaslan, Ozge

    2013-01-01

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

  15. Causal knowledge and the development of inductive reasoning.

    Science.gov (United States)

    Bright, Aimée K; Feeney, Aidan

    2014-06-01

    We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Violation of causality in f( T) gravity

    Science.gov (United States)

    Otalora, G.; Rebouças, M. J.

    2017-11-01

    In the standard formulation, the f( T) field equations are not invariant under local Lorentz transformations, and thus the theory does not inherit the causal structure of special relativity. Actually, even locally violation of causality can occur in this formulation of f( T) gravity. A locally Lorentz covariant f( T) gravity theory has been devised recently, and this local causality problem seems to have been overcome. The non-locality question, however, is left open. If gravitation is to be described by this covariant f( T) gravity theory there are a number of issues that ought to be examined in its context, including the question as to whether its field equations allow homogeneous Gödel-type solutions, which necessarily leads to violation of causality on non-local scale. Here, to look into the potentialities and difficulties of the covariant f( T) theories, we examine whether they admit Gödel-type solutions. We take a combination of a perfect fluid with electromagnetic plus a scalar field as source, and determine a general Gödel-type solution, which contains special solutions in which the essential parameter of Gödel-type geometries, m^2, defines any class of homogeneous Gödel-type geometries. We show that solutions of the trigonometric and linear classes (m^2 electromagnetic field matter component. We extended to the context of covariant f( T) gravity a theorem which ensures that any perfect-fluid homogeneous Gödel-type solution defines the same set of Gödel tetrads h_A^{ μ } up to a Lorentz transformation. We also showed that the single massless scalar field generates Gödel-type solution with no closed time-like curves. Even though the covariant f( T) gravity restores Lorentz covariance of the field equations and the local validity of the causality principle, the bare existence of the Gödel-type solutions makes apparent that the covariant formulation of f( T) gravity does not preclude non-local violation of causality in the form of closed time

  17. CAUSAL RELATIONSHIPS BETWEEN GRAIN, MEAT PRICES AND EXCHANGE RATES

    Directory of Open Access Journals (Sweden)

    Naveen Musunuru

    2017-10-01

    Full Text Available Understanding agricultural commodity price relationships are important as they help producers improve their awareness regarding production costs and ultimately aid in income determination. The present paper empirically examines the dynamic interrelationships among grain, meat prices and the U.S. dollar exchange rate. Johansen cointegration tests reveal no cointegrating relationships among the study variables. Majority of the commodities studied in the paper exhibited unidirectional causality except for corn and lean hogs. The vector autoregression (VAR model results indicate that the grain and meat prices are influenced by their own past prices. The role of exchange rates is found to be limited in linking the agricultural commodities.

  18. Petri Games: Synthesis of Distributed Systems with Causal Memory

    Directory of Open Access Journals (Sweden)

    Bernd Finkbeiner

    2014-08-01

    Full Text Available We present a new multiplayer game model for the interaction and the flow of information in a distributed system. The players are tokens on a Petri net. As long as the players move in independent parts of the net, they do not know of each other; when they synchronize at a joint transition, each player gets informed of the causal history of the other player. We show that for Petri games with a single environment player and an arbitrary bounded number of system players, deciding the existence of a safety strategy for the system players is EXPTIME-complete.

  19. How can we cope with the complexity of the environment? A "Learning by modelling" approach using qualitative reasoning for developing causal models and simulations with focus on Sustainable River Catchment Management

    Science.gov (United States)

    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

  20. Causal Factors for The Adoption Innovation Teacher’s Tv for Teachersand Educational Personnel

    Directory of Open Access Journals (Sweden)

    Thanadol Phuseerit

    2016-12-01

    Full Text Available The purposes of this research were: to 1 study factors for the adoption of the innovation teacher’s TV for Teachers and educational personnel 2 develop and examine consideration of the causal factors for the adoption of the innovation teacher’s TV for teachers and educational personnel model, 3 evaluate and approve causal factors for the adoption of the innovation teacher’s TV for Teachers and educational personnel by the specialists. The method: Step Were 1 Review the literature of the principles, theories and research on the causal factors for the adoption of innovation in education, also study teachers’ adoption of innovation teacher’s TV and causal factors for adoption of innovation teacher’s TV. Further were, analysis and content to collect data on the volume of queries. Collected which was from in-depth interviews. through focus groups, teachers and educational personnel. Identify factors that are associated with the adoption of innovation teacher’s TV. 2 to develop causal factors for the adoption of the innovation teacher’s TV for teachers and educational personnel model. 3 Check the consistency of the causal factors for the adoption of the innovation teacher’s TV for teachers and educational personnel model by experts 4 evaluate and approve causal factors to the adoption innovation teacher’s TV for teachers and educational personnel model from the specialists. The sample were 11 experts in innovation and educational technology. The sample were 450 people whith were Analyzed by Confirmatory Factor Analysis: CFA of Structural Equation Modeling: SEM of causal factor for the adoption of the innovation teacher’s TV for teachers and education personnel model. The instrument used in this study were: 1 a questionnaire causal factor for the adoption of the innovation teacher’s TV for teachers and education personnel, 2 semi-structured questionnaire for interviewing teachers and education personnel, 3 open ended question

  1. Violence in psychosis: conceptualizing its causal relationship with risk factors

    NARCIS (Netherlands)

    Lamsma, J.; Harte, J.M.

    2015-01-01

    Background: While statistically robust, the association between psychosis and violence remains causally unexplained. Objective: To provide an overview of possible causal pathways between risk factors and violence in psychosis. Methods: A structured narrative review of relevant studies published

  2. Elements of Causal Inference: Foundations and Learning Algorithms

    DEFF Research Database (Denmark)

    Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard

    A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...

  3. The Hankel transform of causal distributions

    Directory of Open Access Journals (Sweden)

    Manuel A. Aguirre T.

    2012-03-01

    Full Text Available In this note we evaluate the unidimensional distributional Hankel transform of \\dfrac{x^{\\alpha-1}_{+}}{\\Gamma^{\\alpha}},\\dfrac{x^{\\alpha-1}_{-}}{\\Gamma^{\\alpha}},dfrac{|x|^{\\alpha-1}}{\\Gamma^{\\frac{\\alpha}{2}}},dfrac{|x|^{\\alpha-1}sgn(x}{\\Gamma^{\\frac{\\alpha +1}{2}}} and (x± i0^{\\alpha-1} and then we extend the formulae to certain kinds of n-dimensional distributions calles "causal" and "anti-causal" distributions. We evaluate the distributional Handel transform of \\dfrac{(m^2+P^{\\alpha -1}_{-}}{\\Gamma^{(\\alpha} }, \\dfrac{|m^2+P|^{\\alpha -1}_{-}}{\\Gamma^{(\\frac{\\alpha}{2}}}, \\dfrac{|m^2+P|^{\\alpha -1}sgn(m^2+P}{\\Gamma (\\frac{\\alpha +1}{2 }} and (m^2+P±i0^{\\alpha-1}

  4. Kernel Method for Nonlinear Granger Causality

    Science.gov (United States)

    Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2008-04-01

    Important information on the structure of complex systems can be obtained by measuring to what extent the individual components exchange information among each other. The linear Granger approach, to detect cause-effect relationships between time series, has emerged in recent years as a leading statistical technique to accomplish this task. Here we generalize Granger causality to the nonlinear case using the theory of reproducing kernel Hilbert spaces. Our method performs linear Granger causality in the feature space of suitable kernel functions, assuming arbitrary degree of nonlinearity. We develop a new strategy to cope with the problem of overfitting, based on the geometry of reproducing kernel Hilbert spaces. Applications to coupled chaotic maps and physiological data sets are presented.

  5. Finite quantum electrodynamics the causal approach

    CERN Document Server

    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...

  6. Detecting causal drivers and empirical prediction of the Indian Summer Monsoon

    Science.gov (United States)

    Di Capua, G.; Vellore, R.; Raghavan, K.; Coumou, D.

    2017-12-01

    The Indian summer monsoon (ISM) is crucial for the economy, society and natural ecosystems on the Indian peninsula. Predict the total seasonal rainfall at several months lead time would help to plan effective water management strategies, improve flood or drought protection programs and prevent humanitarian crisis. However, the complexity and strong internal variability of the ISM circulation system make skillful seasonal forecasting challenging. Moreover, to adequately identify the low-frequency, and far-away processes which influence ISM behavior novel tools are needed. We applied a Response-Guided Causal Precursor Detection (RGCPD) scheme, which is a novel empirical prediction method which unites a response-guided community detection scheme with a causal discovery algorithm (CEN). These tool allow us to assess causal pathways between different components of the ISM circulation system and with far-away regions in the tropics, mid-latitudes or Arctic. The scheme has successfully been used to identify causal precursors of the Stratospheric polar vortex enabling skillful predictions at (sub) seasonal timescales (Kretschmer et al. 2016, J.Clim., Kretschmer et al. 2017, GRL). We analyze observed ISM monthly rainfall over the monsoon trough region. Applying causal discovery techniques, we identify several causal precursor communities in the fields of 2m-temperature, sea level pressure and snow depth over Eurasia. Specifically, our results suggest that surface temperature conditions in both tropical and Arctic regions contribute to ISM variability. A linear regression prediction model based on the identified set of communities has good hindcasting skills with 4-5 months lead times. Further we separate El Nino, La Nina and ENSO-neutral years from each other and find that the causal precursors are different dependent on ENSO state. The ENSO-state dependent causal precursors give even higher skill, especially for La Nina years when the ISM is relatively strong. These

  7. Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data.

    Science.gov (United States)

    Cheek, Camden; Zheng, Huiyong; Hallstrom, Brian R; Hughes, Richard E

    2018-01-01

    Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of "outlier" devices that have an especially high risk of reoperation ("revision"). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outlier implants such as the ASR metal-on-metal hip resurfacing device that was recalled. Although identifying an implant as an outlier implies a causal relationship between the implant and revision risk, traditional signal detection methods use classical biostatistical methods. The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.

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

    Science.gov (United States)

    De Gooijer, Jan G.; Sivarajasingham, Selliah

    2008-04-01

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

  9. Does the stock of monei have any causal significance?

    Directory of Open Access Journals (Sweden)

    Philip Arestis

    2003-06-01

    Full Text Available Recent developments in macroeconomics, and economic policy in general, have produced a "new consensus" economy-wide model, in which the stock of money doesnot play any causal role. The stock of money operates a mere residual in the economic process. The absence of the stock of money in many current debates over monetary policy has prompted the statement by the Governor of the Bank of England (during his time as the Deputy Governor that it is ironic that as central banks became more and more concerned with price stability, less and less attention is paid to money. Indeed, the decline of interest in money appeared to have coincided with low inflation in a significant number of countries. This has prompted a number of contributions that, wittingly or perhaps unwittingly, have attempted to "reinstate" a more substantial role for money in this "new" macroeconomics. In this paper we wish to argue that these attempts to "reinstate" money in current macroeconomic thinking entails two important problems. The first is that they contradict an important theoretical property of the new "consensus" macroeconomic model, namely that of dichotomy between the monetary and the real sector. The second is that some of these attempts either fail in terms of their objective, or merely reintroduce the problem rather than solving it. We conclude that if money is to be given a causal role in the "new" consensus model, then a great deal of more substantial research would be necessary.

  10. Inverse odds ratio-weighted estimation for causal mediation analysis.

    Science.gov (United States)

    Tchetgen Tchetgen, Eric J

    2013-11-20

    An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio-weighted approach to estimate so-called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright © 2013 John Wiley & Sons, Ltd.

  11. Granger-Causality Maps of Diffusion Processes

    Czech Academy of Sciences Publication Activity Database

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

    2016-01-01

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

  12. On the causality relations in thermoelectricity

    Science.gov (United States)

    Vázquez, Federico; López de Haro, Mariano; Figueroa, Aldo

    2018-01-01

    The relationship between the causality principle and the existence of couplings between different thermodynamic driving forces in a given phenomenon is discussed. The case of thermoelectricity is explicitly analyzed. A transport equation for the propagation of thermal disturbances in a sample after an electric potential difference is applied is derived. The consequences of the non-hyperbolic character of this equation and the need for investigating its possible connection with nonequilibrium thermodynamics formulations are pointed out.

  13. Causal Relationship between Construction Production and GDP in Turkey

    OpenAIRE

    Hakkı Kutay Bolkol

    2015-01-01

    This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship). Find...

  14. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

    Directory of Open Access Journals (Sweden)

    Daniel Ho

    2011-08-01

    Full Text Available MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007 for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

  15. A new approach to causality in the frequency domain

    OpenAIRE

    Mehmet Dalkir

    2004-01-01

    This study refers to the earlier work of analysis in the frequency domain. A different definition of causality is made, and its implications to the general idea of causality are discussed. The causality relationship between two monetary aggregates, simple sum and Divisia indices, and their relation with the personal income is analyzed using wavelet time-scale decomposition.

  16. Pathway Analysis and the Search for Causal Mechanisms

    Science.gov (United States)

    Weller, Nicholas; Barnes, Jeb

    2016-01-01

    The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting…

  17. Causal structure in categorical quantum mechanics

    Science.gov (United States)

    Lal, Raymond Ashwin

    Categorical quantum mechanics is a way of formalising the structural features of quantum theory using category theory. It uses compound systems as the primitive notion, which is formalised by using symmetric monoidal categories. This leads to an elegant formalism for describing quantum protocols such as quantum teleportation. In particular, categorical quantum mechanics provides a graphical calculus that exposes the information flow of such protocols in an intuitive way. However, the graphical calculus also reveals surprising features of these protocols; for example, in the quantum teleportation protocol, information appears to flow `backwards-in-time'. This leads to question of how causal structure can be described within categorical quantum mechanics, and how this might lead to insight regarding the structural compatibility between quantum theory and relativity. This thesis is concerned with the project of formalising causal structure in categorical quantum mechanics. We begin by studying an abstract view of Bell-type experiments, as described by `no-signalling boxes', and we show that under time-reversal no-signalling boxes generically become signalling. This conflicts with the underlying symmetry of relativistic causal structure. This leads us to consider the framework of categorical quantum mechanics from the perspective of relativistic causal structure. We derive the properties that a symmetric monoidal category must satisfy in order to describe systems in such a background causal structure. We use these properties to define a new type of category, and this provides a formal framework for describing protocols in spacetime. We explore this new structure, showing how it leads to an understanding of the counter-intuitive information flow of protocols in categorical quantum mechanics. We then find that the formal properties of our new structure are naturally related to axioms for reconstructing quantum theory, and we show how a reconstruction scheme based on

  18. 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.

  19. Energy consumption and economic growth in China: A multivariate causality test

    International Nuclear Information System (INIS)

    Wang Yuan; Wang Yichen; Zhou Jing; Zhu Xiaodong; Lu Genfa

    2011-01-01

    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.

  20. Palliative medicine specialists' causal explanations for depression in the palliative care setting: a qualitative in-depth interview study.

    Science.gov (United States)

    Ng, Felicity; Crawford, Gregory B; Chur-Hansen, Anna

    2016-06-01

    Medical practitioners have different causal explanations for depression, and may have greater difficulty in explaining causality of depression in the palliative care setting. The objective of this study was to investigate and describe the causal explanations of depression in the palliative care setting, from the perspective of palliative medicine specialists. Palliative medicine specialists practising in Australia were recruited and purposively sampled. Individual semistructured, in-depth interviews were conducted to explore their explanatory models of depression, including a focus on causal explanations. Nine participants were interviewed to reach data saturation. Interview transcripts were analysed for themes. Six themes for causal explanations of depression were identified: (1) Depression is inexplicable; (2) Biological explanations-primarily neurotransmitter depletion; (3) Psychological explanations-including reaction to circumstances, inability to accept illness and dying, diminished self, and coping mechanisms; (4) Social explanations-including inadequate social support, and contribution from modern medicine and societal norms; (5) Interrelationships between causal factors-mainly multifactoriality; (6) Different explanation for de novo and pre-existing depressions. Participants also articulated a link between causal explanations and clinical interventions. Palliative medicine specialists hold causal explanations of depression that align with the biopsychosocial and vulnerability-stress models. They use multiple individual explanations with diverse theoretical underpinnings, and largely view depression as multifactorial in causality. Given that causal explanations are linked to clinical interventions, these findings have implications for clinical practice and medical education. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  1. Developing Causal Understanding with Causal Maps: The Impact of Total Links, Temporal Flow, and Lateral Position of Outcome Nodes

    Science.gov (United States)

    Jeong, Allan; Lee, Woon Jee

    2012-01-01

    This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…

  2. A time domain frequency-selective multivariate Granger causality approach.

    Science.gov (United States)

    Leistritz, Lutz; Witte, Herbert

    2016-08-01

    The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

  3. Causality, Analyticity and an IR Obstruction to UV Completion

    CERN Document Server

    Adams, A; Dubovsky, S; Nicolis, A; Rattazzi, R; Adams, Allan; Arkani-Hamed, Nima; Dubovsky, Sergei; Nicolis, Alberto; Rattazzi, Riccardo

    2006-01-01

    We argue that certain apparently consistent low-energy effective field theories described by local, Lorentz-invariant Lagrangians, secretly exhibit macroscopic non-locality and cannot be embedded in any UV theory whose S-matrix satisfies canonical analyticity constraints. The obstruction involves the signs of a set of leading irrelevant operators, which must be strictly positive to ensure UV analyticity. An IR manifestation of this restriction is that the "wrong" signs lead to superluminal fluctuations around non-trivial backgrounds, making it impossible to define local, causal evolution, and implying a surprising IR breakdown of the effective theory. Such effective theories can not arise in quantum field theories or weakly coupled string theories, whose S-matrices satisfy the usual analyticity properties. This conclusion applies to the DGP brane-world model modifying gravity in the IR, giving a simple explanation for the difficulty of embedding this model into controlled stringy backgrounds, and to models of...

  4. Causal Association of Overall Obesity and Abdominal Obesity with Type 2 Diabetes: A Mendelian Randomization Analysis.

    Science.gov (United States)

    Wang, Tao; Zhang, Rong; Ma, Xiaojing; Wang, Shiyun; He, Zhen; Huang, Yeping; Xu, Bo; Li, Yangyang; Zhang, Hong; Jiang, Feng; Bao, Yuqian; Hu, Cheng; Jia, Weiping

    2018-05-01

    This study aimed to compare the causal effect of overall obesity and abdominal obesity on type 2 diabetes among Chinese Han individuals. The causal relationship of BMI and waist-to-hip ratio (WHR) with the risk of glucose deterioration and glycemic traits was compared using two different genetic instruments based on 30 BMI loci and 6 WHR loci with Mendelian randomization (MR) in three prospective cohorts (n = 6,476). Each 1-SD genetically instrumented higher WHR was associated with a 65.7% higher risk of glucose deterioration (95% CI = 1.069-2.569, P = 0.024), whereas no significant association of BMI with glucose deterioration was observed. Furthermore, a causal relationship was found only between BMI and homeostatic model assessment β-cell function (HOMA-B) (β = 0.143, P = 0.001), and there was a nominal association with Stumvoll second-phase insulin secretion traits (β = 0.074, P = 0.022). The significance level did not persist in sensitivity analyses, except in the causal estimate of WHR on the Gutt index in MR-Egger (β = -0.379, P = 0.022) and the causal estimate of BMI on homeostatic model assessment β-cell function in weighted median MR (β = 0.128, P = 0.017). The data from this study support the potential causal relationship between abdominal obesity and hyperglycemia, which may be driven by aggravated insulin resistance, in contrast with the potential causal relationship between overall obesity and insulin secretion. © 2018 The Obesity Society.

  5. Information causality from an entropic and a probabilistic perspective

    International Nuclear Information System (INIS)

    Al-Safi, Sabri W.; Short, Anthony J.

    2011-01-01

    The information causality principle is a generalization of the no-signaling principle which implies some of the known restrictions on quantum correlations. But despite its clear physical motivation, information causality is formulated in terms of a rather specialized game and figure of merit. We explore different perspectives on information causality, discussing the probability of success as the figure of merit, a relation between information causality and the nonlocal ''inner-product game,'' and the derivation of a quadratic bound for these games. We then examine an entropic formulation of information causality with which one can obtain the same results, arguably in a simpler fashion.

  6. Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

    Science.gov (United States)

    Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen

    2017-04-01

    Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

  7. Indications of de Sitter spacetime from classical sequential growth dynamics of causal sets

    International Nuclear Information System (INIS)

    Ahmed, Maqbool; Rideout, David

    2010-01-01

    A large class of the dynamical laws for causal sets described by a classical process of sequential growth yields a cyclic universe, whose cycles of expansion and contraction are punctuated by single 'origin elements' of the causal set. We present evidence that the effective dynamics of the immediate future of one of these origin elements, within the context of the sequential growth dynamics, yields an initial period of de Sitter-like exponential expansion, and argue that the resulting picture has many attractive features as a model of the early universe, with the potential to solve some of the standard model puzzles without any fine-tuning.

  8. Causal inference with missing exposure information: Methods and applications to an obstetric study.

    Science.gov (United States)

    Zhang, Zhiwei; Liu, Wei; Zhang, Bo; Tang, Li; Zhang, Jun

    2016-10-01

    Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and for causal inference with complete data (i.e. no unmeasured confounding and another positivity assumption). These methods involve three models: one for treatment assignment, one for the dependence of outcome on treatment and covariates, and one for the missing data mechanism. In general, consistent estimation of causal quantities requires correct specification of at least two of the three models, although there may be some flexibility as to which two models need to be correct. Such flexibility is afforded by doubly robust estimators adapted from the missing covariates literature and the literature on causal inference with complete data, and by a newly developed triply robust estimator that is consistent if any two of the three models are correct. The methods are applied to the Consortium on Safe Labor data and compared in a simulation study mimicking the Consortium on Safe Labor. © The Author(s) 2013.

  9. Emergent Geometry from Entropy and Causality

    Science.gov (United States)

    Engelhardt, Netta

    In this thesis, we investigate the connections between the geometry of spacetime and aspects of quantum field theory such as entanglement entropy and causality. This work is motivated by the idea that spacetime geometry is an emergent phenomenon in quantum gravity, and that the physics responsible for this emergence is fundamental to quantum field theory. Part I of this thesis is focused on the interplay between spacetime and entropy, with a special emphasis on entropy due to entanglement. In general spacetimes, there exist locally-defined surfaces sensitive to the geometry that may act as local black hole boundaries or cosmological horizons; these surfaces, known as holographic screens, are argued to have a connection with the second law of thermodynamics. Holographic screens obey an area law, suggestive of an association with entropy; they are also distinguished surfaces from the perspective of the covariant entropy bound, a bound on the total entropy of a slice of the spacetime. This construction is shown to be quite general, and is formulated in both classical and perturbatively quantum theories of gravity. The remainder of Part I uses the Anti-de Sitter/ Conformal Field Theory (AdS/CFT) correspondence to both expand and constrain the connection between entanglement entropy and geometry. The AdS/CFT correspondence posits an equivalence between string theory in the "bulk" with AdS boundary conditions and certain quantum field theories. In the limit where the string theory is simply classical General Relativity, the Ryu-Takayanagi and more generally, the Hubeny-Rangamani-Takayanagi (HRT) formulae provide a way of relating the geometry of surfaces to entanglement entropy. A first-order bulk quantum correction to HRT was derived by Faulkner, Lewkowycz and Maldacena. This formula is generalized to include perturbative quantum corrections in the bulk at any (finite) order. Hurdles to spacetime emergence from entanglement entropy as described by HRT and its quantum

  10. Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples.

    Directory of Open Access Journals (Sweden)

    Yoshito Hirata

    Full Text Available Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly.

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

    Directory of Open Access Journals (Sweden)

    Hazuki Ishida

    2013-01-01

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

  12. A Causal Theory of Mnemonic Confabulation.

    Science.gov (United States)

    Bernecker, Sven

    2017-01-01

    This paper attempts to answer the question of what defines mnemonic confabulation vis-à-vis genuine memory. The two extant accounts of mnemonic confabulation as "false memory" and as ill-grounded memory are shown to be problematic, for they cannot account for the possibility of veridical confabulation, ill-grounded memory, and well-grounded confabulation. This paper argues that the defining characteristic of mnemonic confabulation is that it lacks the appropriate causal history. In the confabulation case, there is no proper counterfactual dependence of the state of seeming to remember on the corresponding past representation.

  13. A Causal Theory of Mnemonic Confabulation

    Directory of Open Access Journals (Sweden)

    Sven Bernecker

    2017-07-01

    Full Text Available This paper attempts to answer the question of what defines mnemonic confabulation vis-à-vis genuine memory. The two extant accounts of mnemonic confabulation as “false memory” and as ill-grounded memory are shown to be problematic, for they cannot account for the possibility of veridical confabulation, ill-grounded memory, and well-grounded confabulation. This paper argues that the defining characteristic of mnemonic confabulation is that it lacks the appropriate causal history. In the confabulation case, there is no proper counterfactual dependence of the state of seeming to remember on the corresponding past representation.

  14. De Broglie's causal interpretations of quantum mechanics

    International Nuclear Information System (INIS)

    YOAV Ben-Dov

    1989-01-01

    In this article we trace the history of de Broglie's two causal interpretations of quantum mechanics, namely the double solution and the pilot wave theories, at the two periods in which he developed them: 1924-27 and 1952 onwards. Examining the reasons for which he always preferred the first theory to the second, reasons that are mainly concerned with the question of the physical nature of the quantum wave function, we try to show the continuity and the coherence of his underlying vision

  15. [Therapy of polyneuropathies. Causal and symptomatic].

    Science.gov (United States)

    Müller-Felber, W

    2001-05-28

    In the first instance, polyneuropathies are treated causally. The most common underlying cause is diabetes mellitus or alcohol abuse. In a large number of patients with polyneuropathy, however, the underlying cause cannot be definitively identified. For these--but equally for patients with etiologically clear polyneuropathy--a stock-taking of clinical symptoms should be carried out and, where indicated, symptomatic treatment initiated. In addition to medication aimed at combating pain, muscular spasm, autonomic functional disorders, and for the prevention of thrombosis, physical measures (physiotherapy, foot care, orthopedic shoes) are of primary importance.

  16. Conditional Granger Causality of Diffusion Processes

    Czech Academy of Sciences Publication Activity Database

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

    2017-01-01

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

  17. A Bayesian semiparametric latent variable approach to causal mediation.

    Science.gov (United States)

    Kim, Chanmin; Daniels, Michael; Li, Yisheng; Milbury, Kathrin; Cohen, Lorenzo

    2018-03-30

    In assessing causal mediation effects in randomized studies, a challenge is that the direct and indirect effects can vary across participants due to different measured and unmeasured characteristics. In that case, the population effect estimated from standard approaches implicitly averages over and does not estimate the heterogeneous direct and indirect effects. We propose a Bayesian semiparametric method to estimate heterogeneous direct and indirect effects via clusters, where the clusters are formed by both individual covariate profiles and individual effects due to unmeasured characteristics. These cluster-specific direct and indirect effects can be estimated through a set of regression models where specific coefficients are clustered by a stick-breaking prior. To let clustering be appropriately informed by individual direct and indirect effects, we specify a data-dependent prior. We conduct simulation studies to assess performance of the proposed method compared to other methods. We use this approach to estimate heterogeneous causal direct and indirect effects of an expressive writing intervention for patients with renal cell carcinoma. Copyright © 2017 John Wiley & Sons, Ltd.

  18. Learning Wireless Networks' Topologies Using Asymmetric Granger Causality

    Science.gov (United States)

    Laghate, Mihir; Cabric, Danijela

    2018-02-01

    Sharing spectrum with a communicating incumbent user (IU) network requires avoiding interference to IU receivers. But since receivers are passive when in the receive mode and cannot be detected, the network topology can be used to predict the potential receivers of a currently active transmitter. For this purpose, this paper proposes a method to detect the directed links between IUs of time multiplexing communication networks from their transmission start and end times. It models the response mechanism of commonly used communication protocols using Granger causality: the probability of an IU starting a transmission after another IU's transmission ends increases if the former is a receiver of the latter. This paper proposes a non-parametric test statistic for detecting such behavior. To help differentiate between a response and the opportunistic access of available spectrum, the same test statistic is used to estimate the response time of each link. The causal structure of the response is studied through a discrete time Markov chain that abstracts the IUs' medium access protocol and focuses on the response time and response probability of 2 IUs. Through NS-3 simulations, it is shown that the proposed algorithm outperforms existing methods in accurately learning the topologies of infrastructure-based networks and that it can infer the directed data flow in ad hoc networks with finer time resolution than an existing method.

  19. [Analysis of characteristics of alpha electroencephalogram during the interaction between emotion and cognition based on Granger causality].

    Science.gov (United States)

    Wang, Ning; Wei, Ling; Li, Yingjie

    2012-12-01

    Studying the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain and nowadays, it has become a hot topic in cognitive neuroscience. Granger causality (GC), based on multivariate autoregressive (MVAR) model, and being able to be used to analyse causal characteristic of brain regions has been widely used in electroencephalography (EEG) in event-related paradigms research. In this study, we recorded the EEGs from 13 normal subjects (6 males and 7 females) during emotional face search task. We utilized Granger causality to establish a causal model of different brain areas under different rhythms at specific stages of cognition, and then convinced the brain dynamic network topological properties in the process of emotion and cognition. Therefore, we concluded that in the alpha band, (1) negative emotion face induced larger causal effects than positive ones; (2) 100-200ms emotional signal was the most prominent ones while 300-400ms and 700-800ms would take the second place; (3) The rear brain region modulated the front in the process of causal modulation; (4) The frontal and pillow area involved in the brain causal modulation as a key brain area; and (5) Negative partiality existed in the information processing, especially during 0-100ms after the negative expression stimulation.

  20. Pseudo-conditionals and causal assertibles in Stoic logic

    Directory of Open Access Journals (Sweden)

    Miguel López-Astorga

    2016-09-01

    Full Text Available http://dx.doi.org/ 10.5007/1808-1711.2016v20n3p417 The Stoics not only analyzed sentences showing to be clear conditionals. They also reviewed other kinds of sentences related to the conditional that are not exactly conditionals, for example, the pseudo-conditionals and the causal assertibles. In this paper, I try to argue that the Stoic account of such sentences reveals that certain problematic issues that contemporary cognitive science is concerned with, such as the ways the conditionals can be expressed or the pragmatic phenomenon of the conditional perfection, were already studied by the Stoics, and that they even gave their solutions to those problems. To do that, I resort to the semantic analysis of models usually made by the mental models theory, and use it as a methodological tool.

  1. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  2. Are bruxism and the bite causally related?

    Science.gov (United States)

    Lobbezoo, F; Ahlberg, J; Manfredini, D; Winocur, E

    2012-07-01

    In the dental profession, the belief that bruxism and dental (mal-)occlusion ('the bite') are causally related is widespread. The aim of this review was to critically assess the available literature on this topic. A PubMed search of the English-language literature, using the query 'Bruxism [Majr] AND (Dental Occlusion [Majr] OR Malocclusion [Majr])', yielded 93 articles, of which 46 papers were finally included in the present review*. Part of the included publications dealt with the possible associations between bruxism and aspects of occlusion, from which it was concluded that neither for occlusal interferences nor for factors related to the anatomy of the oro-facial skeleton, there is any evidence available that they are involved in the aetiology of bruxism. Instead, there is a growing awareness of other factors (viz. psychosocial and behavioural ones) being important in the aetiology of bruxism. Another part of the included papers assessed the possible mediating role of occlusion between bruxism and its purported consequences (e.g. tooth wear, loss of periodontal tissues, and temporomandibular pain and dysfunction). Even though most dentists agree that bruxism may have several adverse effects on the masticatory system, for none of these purported adverse effects, evidence for a mediating role of occlusion and articulation has been found to date. Hence, based on this review, it should be concluded that to date, there is no evidence whatsoever for a causal relationship between bruxism and the bite. © 2012 Blackwell Publishing Ltd.

  3. [Antibibiotic resistance by nosocomial infections' causal agents].

    Science.gov (United States)

    Salazar-Holguín, Héctor Daniel; Cisneros-Robledo, María Elena

    2016-01-01

    The antibibiotic resistance by nosocomial infections (NI) causal agents constitutes a seriously global problematic that involves the Mexican Institute of Social Security's Regional General Hospital 1 in Chihuahua, Mexico; although with special features that required to be specified and evaluated, in order to concrete an effective therapy. Observational, descriptive and prospective study; by means of active vigilance all along 2014 in order to detect the nosocomial infections, for epidemiologic study, culture and antibiogram to identify its causal agents and antibiotics resistance and sensitivity. Among 13527 hospital discharges, 1079 displayed NI (8 %), standed out: the related on vascular lines, of surgical site, pneumonia and urinal track; they added up two thirds of the total. We carried out culture and antibiogram about 300 of them (27.8 %); identifying 31 bacterian species, mainly seven of those (77.9 %): Escherichia coli, Staphylococcus aureus and epidermidis, Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae and Enterobacter cloacae; showing multiresistance to 34 tested antibiotics, except in seven with low or without resistance at all: vancomycin, teicoplanin, linezolid, quinupristin-dalfopristin, piperacilin-tazobactam, amikacin and carbapenems. When we contrasted those results with the recommendations in the clinical practice guides, it aroused several contradictions; so they must be taken with reserves and has to be tested in each hospital, by means of cultures and antibiograms in practically every case of nosocomial infection.

  4. Introducing mechanics by tapping core causal knowledge

    International Nuclear Information System (INIS)

    Klaassen, Kees; Westra, Axel; Emmett, Katrina; Eijkelhof, Harrie; Lijnse, Piet

    2008-01-01

    This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force causes a deviation from how an object would move of its own accord (i.e. its force-free motion), and (b) an incentive to search, where the motion deviates from the assumed force-free motion, for recurring configurations with which such deviations can be correlated (interaction theory). Various assumptions can be made concerning both the force-free motion and the interaction theory, thus giving rise to a variety of specific explanations. Kepler's semi-implicit intuition about the force-free motion is rest, Newton's explicit assumption is uniform rectilinear motion, while in everyday explanations a diversity of pragmatic suggestions can be recognized. The idea is that the explanatory strategy, once made explicit by drawing on students' intuitive causal knowledge, can be made to function for students as an advance organizer, in the sense of a general scheme that they recognize but do not yet know how to detail for scientific purposes

  5. ¿CONFIEREN PODERES CAUSALES LOS UNIVERSALES TRASCENDENTES?

    Directory of Open Access Journals (Sweden)

    José Tomás Alvarado Marambio

    2013-11-01

    Full Text Available This work discusses the so-called ‘Eleatic’ argument against the existence of transcendent universals, i. e. universals which does not require instantiation for its existence. The Eleatic Principle states that everything produces a difference in the causal powers of something. As transcendent universals seem not to produce such a difference, transcendent universals seem not to exist. The argument depends crucially on the justification and the interpretation of the Eleatic Principle. It is argued, first, that it is not very clear that the principle is justified, and, second, that there are several alternatives for its interpretation, in relation with the different theories one can endorse about modality or causality. Anti-realist theories of modality or causality are not very appropriate for the understanding of what should be a ‘causal power’. Neither does a realist theory of causality conjoined with a combinatorial theory of possible worlds. A ‘causal power’ seems to be better understood in connection with a realist –non-reductionist– theory of causality and a causal theory of modality. Taken in this way the Eleatic Principle, nonetheless, it is argued that transcendent universals do ‘produce’ a difference in causal powers, for every causal connection requires such universals for its existence.

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

    International Nuclear Information System (INIS)

    Solarin, Sakiru Adebola; Shahbaz, Muhammad

    2013-01-01

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

  7. God Does Not Play Dice: Causal Determinism and Preschoolers' Causal Inferences

    Science.gov (United States)

    Schulz, Laura E.; Sommerville, Jessica

    2006-01-01

    Three studies investigated children's belief in causal determinism. If children are determinists, they should infer unobserved causes whenever observed causes appear to act stochastically. In Experiment 1, 4-year-olds saw a stochastic generative cause and inferred the existence of an unobserved inhibitory cause. Children traded off inferences…

  8. Quantum causality conceptual issues in the causal theory of quantum mechanics

    CERN Document Server

    Riggs, Peter J; French, Steven RD

    2009-01-01

    This is a treatise devoted to the foundations of quantum physics and the role that causality plays in the microscopic world governed by the laws of quantum mechanics. The book is controversial and will engender some lively debate on the various issues raised.

  9. The Ineliminable Distortion of Reality: On Causality, Representation, Abstraction and Idealization in Batterman’s Philosophy of Science

    DEFF Research Database (Denmark)

    Bentzen, Martin Mose

    of emergents having novel causal powers with emergents figuring in novel explanatory stories, see (Batterman, 2002). I argue that Batterman’s view of causality is ontologically too restrictive. I also argue that it is methodologically too restrictive, in that abstractions in science, including some involved......) or that they proceed by appealing to fictional highly idealized models in physics, see (Bokulich, 2011), (Pexton, 2014), (Wayne, 2015). I claim that these arguments do not preclude that causality is presupposed at the macro-level, except perhaps in quantum theoretical explanations in which case an appeal to Bohr...

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

    Science.gov (United States)

    Liang, X. S.

    2017-12-01

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

  11. Specious causal attributions in the social sciences: the reformulated stepping-stone theory of heroin use as exemplar.

    Science.gov (United States)

    Baumrind, D

    1983-12-01

    The claims based on causal models employing either statistical or experimental controls are examined and found to be excessive when applied to social or behavioral science data. An exemplary case, in which strong causal claims are made on the basis of a weak version of the regularity model of cause, is critiqued. O'Donnell and Clayton claim that in order to establish that marijuana use is a cause of heroin use (their "reformulated stepping-stone" hypothesis), it is necessary and sufficient to demonstrate that marijuana use precedes heroin use and that the statistically significant association between the two does not vanish when the effects of other variables deemed to be prior to both of them are removed. I argue that O'Donnell and Clayton's version of the regularity model is not sufficient to establish cause and that the planning of social interventions both presumes and requires a generative rather than a regularity causal model. Causal modeling using statistical controls is of value when it compels the investigator to make explicit and to justify a causal explanation but not when it is offered as a substitute for a generative analysis of causal connection.

  12. Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks.

    Science.gov (United States)

    Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Galka, Andreas; Granert, Oliver; Wolff, Stephan; Deuschl, Guenther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

    2013-01-01

    Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC). Time-resolved partial directed coherence uses the principle of state space modelling to estimate Multivariate Autoregressive (MVAR) coefficients. This method is useful to visualize both frequency and time dynamics of causality between the time series. Afterwards, causality results from different modalities are compared by estimating the Spearman correlation. In to be presented study, we used directionality vectors to analyze correlation, rather than actual signal vectors. Results show that causality analysis of the fMRI correlates more closely to causality results of oxy-NIRS as compared to deoxy-NIRS in case of a finger sequencing task. However, in case of simple finger tapping, no clear difference between oxy-fMRI and deoxy-fMRI correlation is identified.

  13. Learning causal networks with latent variables from multivariate information in genomic data.

    Directory of Open Access Journals (Sweden)

    Louis Verny

    2017-10-01

    Full Text Available Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.

  14. Information Theoretic Approach to Discovering Causalities in the Solar Cycle

    Science.gov (United States)

    Wing, Simon; Johnson, Jay R.; Vourlidas, Angelos

    2018-02-01

    The causal parameters and response lag times of the solar cycle dynamics are investigated with transfer entropy, which can determine the amount of information transfer from one variable to another. The causal dependency of the solar cycle parameters is bidirectional. The transfer of information from the solar polar field to the sunspot number (SSN) peaks at lag time (τ) ∼ 30–40 months, but thereafter it remains at a persistently low level for at least 400 months (∼3 solar cycles) for the period 1906–2014. The latter may lend support to the idea that the polar fields from the last three or more solar cycles can affect the production of the SSN of the subsequent cycle. There is also a similarly long-term information transfer from the SSN to the polar field. Both the meridional flow speed and flux emergence (proxied by the SSN) transfer information to the polar field, but one transfers more information than the other, depending on the lag times. The meridional flow speed transfers more information than the SSN to the polar field at τ ∼ 28–30 months and at τ ∼ 90–110 months, which may be consistent with some flux transfer dynamo models and some surface flux transport models. However, the flux emergence transfers more information than the meridional flow to the polar field at τ ∼ 60–80 months, which may be consistent with a recently developed surface flux transport model. The transfer of information from the meridional flow to the SSN peaks at τ ∼ 110–120 months (∼1 solar cycle).

  15. Reducing Children’s Behavior Problems through Social Capital: A Causal Assessment

    Science.gov (United States)

    López Turley, Ruth N.; Gamoran, Adam; McCarty, Alyn Turner; Fish, Rachel

    2016-01-01

    Behavior problems among young children have serious detrimental effects on short and long-term educational outcomes. An especially promising prevention strategy may be one that focuses on strengthening the relationships among families in schools, or social capital. However, empirical research on social capital has been constrained by conceptual and causal ambiguity. This study attempts to construct a more focused conceptualization of social capital and aims to determine the causal effects of social capital on children’s behavior. Using data from a cluster randomized trial of 52 elementary schools, we apply several multilevel models to assess the causal relationship, including intent to treat and treatment on the treated analyses. Taken together, these analyses provide stronger evidence than previous studies that social capital improves children’s behavioral outcomes and that these improvements are not simply a result of selection into social relations but result from the social relations themselves. PMID:27886729

  16. GMAC: a Matlab toolbox for spectral Granger causality analysis of fMRI data.

    Science.gov (United States)

    Tana, Maria Gabriella; Sclocco, Roberta; Bianchi, Anna Maria

    2012-10-01

    Investigation of causal interactions within brain networks using Granger causality analysis (GCA) is a key challenge in studying neural activity on the basis of functional magnetic resonance imaging (fMRI). The article describes an open-source software toolbox GMAC (Granger multivariate autoregressive connectivity) implementing multivariate spectral GCA. Available features are: fMRI data importing/exporting, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions have been integrated into a user-friendly graphical interface developed in the Matlab environment, easily accessible to both technical and clinical users. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Entanglement, non-Markovianity, and causal non-separability

    Science.gov (United States)

    Milz, Simon; Pollock, Felix A.; Le, Thao P.; Chiribella, Giulio; Modi, Kavan

    2018-03-01

    Quantum mechanics, in principle, allows for processes with indefinite causal order. However, most of these causal anomalies have not yet been detected experimentally. We show that every such process can be simulated experimentally by means of non-Markovian dynamics with a measurement on additional degrees of freedom. In detail, we provide an explicit construction to implement arbitrary a causal processes. Furthermore, we give necessary and sufficient conditions for open system dynamics with measurement to yield processes that respect causality locally, and find that tripartite entanglement and nonlocal unitary transformations are crucial requirements for the simulation of causally indefinite processes. These results show a direct connection between three counter-intuitive concepts: entanglement, non-Markovianity, and causal non-separability.

  18. A MATLAB toolbox for Granger causal connectivity analysis.

    Science.gov (United States)

    Seth, Anil K

    2010-02-15

    Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language. Copyright 2009 Elsevier B.V. All rights reserved.

  19. Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Multi-conditions

    Science.gov (United States)

    Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Wang, Xiaochen; Zhu, Ma

    2017-03-01

    Intelligent diagnosis system is applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing conditional functional events into ordinary DUCG to deal with spacecraft multi-conditions. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

  20. Participation in Athletics and Female Sexual Risk Behavior: The Evaluation of Four Causal Structures.

    Science.gov (United States)

    Dodge, Tonya; Jaccard, James

    2002-01-01

    Compared sexual risk behavior of female athletes and nonathletes. Examined mediation, reverse mediation, spurious effects, and moderated causal models, using as potential mediators physical development, educational aspirations, self-esteem, attitudes toward pregnancy, involvement in a romantic relationship, age, ethnicity, and social class. Found…

  1. A causal analysis framework for land-use change and the potential role of bioenergy policy

    NARCIS (Netherlands)

    Efroymson, Rebecca A.; Kline, Keith L.; Angelsen, Arild; Verburg, Peter H.; Dale, Virginia H.; Langeveld, Johannes W.A.; McBride, Allen

    2016-01-01

    We propose a causal analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for

  2. A Cross-Cultural Validation of Perceived Locus of Causality Scale in Physical Education Context

    Science.gov (United States)

    Wang, C. K. John; Hagger, Martin; Liu, Woon Chia

    2009-01-01

    We examined the validity of the factor structure and invariance of the Perceived Locus of Causality (PLOC) scale instrument scores across two nations endorsing collectivist (Singapore) and individualist (Great Britain) cultural values. Results indicated that confirmatory factor analytic models of the PLOC exhibited adequate ft according to…

  3. Causal attributions, real life-events and personality characteristics : a preliminary study

    NARCIS (Netherlands)

    SANDERMAN, R

    1986-01-01

    The learned-helplessness model has been given much attention recently. In this article some issues are briefly reviewed, the main purpose of this study was, however, to determine the relationship between causal attributions and personality characteristics, symptoms and feelings of well-being.

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

    Science.gov (United States)

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

    2011-01-01

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

  5. Foreign Language Listening Anxiety and Listening Performance: Conceptualizations and Causal Relationships

    Science.gov (United States)

    Zhang, Xian

    2013-01-01

    This study used structural equation modeling to explore the possible causal relations between foreign language (English) listening anxiety and English listening performance. Three hundred participants learning English as a foreign language (FL) completed the foreign language listening anxiety scale (FLLAS) and IELTS test twice with an interval of…

  6. Assessing Causal Pathways between Physical Formidability and Aggression in Human Males

    DEFF Research Database (Denmark)

    Petersen, Michael Bang; Dawes, Christopher T.

    2017-01-01

    Studies suggest the existence of an association between the physical formidability of human males and their level of aggression. This association is theoretically predictable from animal models of conflict behavior but could emerge from multiple different causal pathways. Previous studies have...

  7. Causal Temperature Profiles in Horizon-free Collapse NF Naidu & M ...

    Indian Academy of Sciences (India)

    extended irreversible thermodynamics. We are in a position to obtain exact solutions to the causal heat transport equation for both the special case of constant collision time as well as variable collision time. Our results are in agreement with earlier thermo- dynamical investigations of radiating stellar models. We find that ...

  8. Causal attribution among women with breast cancer

    Directory of Open Access Journals (Sweden)

    Ana Carolina W. B. Peuker

    2016-01-01

    Full Text Available Abstract Causal attribution among women with breast cancer was studied. The study included 157 women outpatients with breast cancer. A form for sociodemographic and clinical data and the Revised Illness Perception Questionnaire (IPQ-R were used. The results showed that women attributed breast cancer primarily to psychological causes, which does not correspond to known multifactorial causes validated by the scientific community. Providing high quality, patient-centered care requires sensitivity to breast cancer women’s beliefs about the causes of their cancer and awareness of how it can influence patient’s health behaviors after diagnosis. If women with breast cancer attribute the illness to modifiable factors then they can keep a healthy lifestyle, improving their recovery and decrease the probability of cancer recurrence after diagnosis.

  9. Delinquency among pathological gamblers: A causal approach.

    Science.gov (United States)

    Meyer, G; Fabian, T

    1992-03-01

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

  10. Causality Constraints in Conformal Field Theory

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d-dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂φ)4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinni...

  11. On the causal set–continuum correspondence

    International Nuclear Information System (INIS)

    Saravani, Mehdi; Aslanbeigi, Siavash

    2014-01-01

    We present two results that concern certain aspects of the question: when is a causal set well approximated by a Lorentzian manifold? The first result is a theorem that shows that the number–volume correspondence, if required to hold even for arbitrarily small regions, is best realized via Poisson sprinkling. The second result concerns a family of lattices in 1+1 dimensional Minkowski space, known as Lorentzian lattices, which we show provide a much better number–volume correspondence than Poisson sprinkling for large volumes. We argue, however, that this feature should not persist in higher dimensions. We conclude by conjecturing a form of the aforementioned theorem that holds under weaker assumptions, namely that Poisson sprinkling provides the best number–volume correspondence in 3+1 dimensions for spacetime regions with macroscopically large volumes. (paper)

  12. Exploring Torus Universes in Causal Dynamical Triangulations

    DEFF Research Database (Denmark)

    Budd, Timothy George; Loll, R.

    2013-01-01

    Motivated by the search for new observables in nonperturbative quantum gravity, we consider Causal Dynamical Triangulations (CDT) in 2+1 dimensions with the spatial topology of a torus. This system is of particular interest, because one can study not only the global scale factor, but also global ....... Apart from setting the stage for the analysis of shape dynamics on the torus, the new set-up highlights the role of nontrivial boundaries and topology....... shape variables in the presence of arbitrary quantum fluctuations of the geometry. Our initial investigation focusses on the dynamics of the scale factor and uncovers a qualitatively new behaviour, which leads us to investigate a novel type of boundary conditions for the path integral. Comparing large...

  13. Equity Theory Ratios as Causal Schemas.

    Science.gov (United States)

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

  14. Equity Theory Ratios as Causal Schemas

    Directory of Open Access Journals (Sweden)

    Alexios Arvanitis

    2016-08-01

    Full Text Available Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

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

    Science.gov (United States)

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

    2009-12-01

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

  16. Causal topology in future and past distinguishing spacetimes

    Science.gov (United States)

    Parrikar, Onkar; Surya, Sumati

    2011-08-01

    The causal structure of a strongly causal spacetime is particularly well endowed. Not only does it determine the conformal spacetime geometry when the spacetime dimension n > 2, as shown by Malament and Hawking-King-McCarthy (MHKM), but also the manifold dimension. The MHKM result, however, applies more generally to spacetimes satisfying the weaker causality condition of future and past distinguishability (FPD), and it is an important question whether the causal structure of such spacetimes can determine the manifold dimension. In this work, we show that the answer to this question is in the affirmative. We investigate the properties of future or past distinguishing spacetimes and show that their causal structures determine the manifold dimension. This gives a non-trivial generalization of the MHKM theorem and suggests that there is a causal topology for FPD spacetimes which encodes manifold dimension and which is strictly finer than the Alexandrov topology. We show that such a causal topology does exist. We construct it using a convergence criterion based on sequences of 'chain intervals' which are the causal analogues of null geodesic segments. We show that when the region of strong causality violation satisfies a local achronality condition, this topology is equivalent to the manifold topology in an FPD spacetime.

  17. Causal topology in future and past distinguishing spacetimes

    Energy Technology Data Exchange (ETDEWEB)

    Parrikar, Onkar [Birla Institute of Technology and Science-Pilani, Goa campus, Goa 403 726 (India); Surya, Sumati, E-mail: ssurya@rri.res.in [Raman Research Institute, CV Raman Ave, Sadashivanagar, Bangalore 560 080 (India)

    2011-08-07

    The causal structure of a strongly causal spacetime is particularly well endowed. Not only does it determine the conformal spacetime geometry when the spacetime dimension n > 2, as shown by Malament and Hawking-King-McCarthy (MHKM), but also the manifold dimension. The MHKM result, however, applies more generally to spacetimes satisfying the weaker causality condition of future and past distinguishability (FPD), and it is an important question whether the causal structure of such spacetimes can determine the manifold dimension. In this work, we show that the answer to this question is in the affirmative. We investigate the properties of future or past distinguishing spacetimes and show that their causal structures determine the manifold dimension. This gives a non-trivial generalization of the MHKM theorem and suggests that there is a causal topology for FPD spacetimes which encodes manifold dimension and which is strictly finer than the Alexandrov topology. We show that such a causal topology does exist. We construct it using a convergence criterion based on sequences of 'chain intervals' which are the causal analogues of null geodesic segments. We show that when the region of strong causality violation satisfies a local achronality condition, this topology is equivalent to the manifold topology in an FPD spacetime.

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

    Science.gov (United States)

    Weed, Douglas L

    2018-05-01

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

  19. A general, multivariate definition of causal effects in epidemiology.

    Science.gov (United States)

    Flanders, W Dana; Klein, Mitchel

    2015-07-01

    Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

  20. A Historical Overview and Contemporary Expansion of Psychological Theories of Determinism, Probabilistic Causality, Indeterminate Free Will, and Moral and Legal Responsibility

    Science.gov (United States)

    Wilks, Duffy; Ratheal, Juli D'Ann

    2009-01-01

    The authors provide a historical overview of the development of contemporary theories of counseling and psychology in relation to determinism, probabilistic causality, indeterminate free will, and moral and legal responsibility. They propose a unique model of behavioral causality that incorporates a theory of indeterminate free will, a concept…