Statistics and Causality

Methods for Applied Empirical Research
Herausgegeben:Wiedermann, Wolfgang; Eye, Alexander von

• Gebundenes Buch

Produktbeschreibung

A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality

Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses.

The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes:
_ New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories
_ End-of-chapter bibliographies that provide references for further discussions and additional research topics
_ Discussions on the use and applicability of software when appropriate

Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.

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Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W118947040
• 1. Auflage
• Seitenzahl: 480
• Erscheinungstermin: 7. Juni 2016
• Englisch
• Abmessung: 236mm x 155mm x 30mm
• Gewicht: 792g
• ISBN-13: 9781118947043
• ISBN-10: 1118947045
• Artikelnr.: 44694468

Autorenporträt

Wolfgang Wiedermann, PhD, is Assistant Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri, Columbia. His research interests include the development of methods for direction dependence analysis and causal inference, the development and evaluation of methods for person-oriented research, and methods for intensive longitudinal data. Alexander von Eye, PhD, is Professor Emeritus of Psychology at Michigan State University. His research interests include statistical methods, categorical data analysis, and human development. Dr. von Eye is Section Editor for the Encyclopedia of Statistics in Behavioral Science and is the coauthor of Log-Linear Modeling: Concepts, Interpretation, and Application , both published by Wiley.

Inhaltsangabe

LIST OF CONTRIBUTORS xiii

PREFACE xvii

ACKNOWLEDGMENTS xxv

PART I BASES OF CAUSALITY 1

1 Causation and the Aims of Inquiry 3
Ned Hall

1.1 Introduction, 3

1.2 The Aim of an Account of Causation, 4

1.3 The Good News, 7

1.4 The Challenging News, 17

1.5 The Perplexing News, 26

2 Evidence and Epistemic Causality 31
Michael Wilde and Jon Williamson

2.1 Causality and Evidence, 31

2.2 The Epistemic Theory of Causality, 35

2.3 The Nature of Evidence, 38

2.4 Conclusion, 40

PART II DIRECTIONALITY OF EFFECTS 43

3 Statistical Inference for Direction of Dependence in Linear Models 45
Yadolah Dodge and Valentin Rousson

3.1 Introduction, 45

3.2 Choosing the Direction of a Regression Line, 46

3.3 Significance Testing for the Direction of a Regression Line, 48

3.4 Lurking Variables and Causality, 54

3.5 Brain and Body Data Revisited, 57

3.6 Conclusions, 60

4 Directionality of Effects in Causal Mediation Analysis 63
Wolfgang Wiedermann and Alexander von Eye

4.1 Introduction, 63

4.2 Elements of Causal Mediation Analysis, 66

4.3 Directionality of Effects in Mediation Models, 68

4.4 Testing Directionality Using Independence Properties of Competing Mediation Models, 71

4.5 Simulating the Performance of Directionality Tests, 82

4.6 Empirical Data Example: Development of Numerical Cognition, 85

4.7 Discussion, 92

5 Direction of Effects in Categorical Variables: A Structural Perspective 107
Alexander von Eye and Wolfgang Wiedermann

5.1 Introduction, 107

5.2 Concepts of Independence in Categorical Data Analysis, 108

5.3 Direction Dependence in Bivariate Settings: Metric and Categorical Variables, 110

5.4 Explaining the Structure of Cross-Classifications, 117

5.5 Data Example, 123

5.6 Discussion, 126

6 Directional Dependence Analysis Using Skew-Normal Copula-Based Regression 131
Seongyong Kim and Daeyoung Kim

6.1 Introduction, 131

6.2 Copula-Based Regression, 133

6.3 Directional Dependence in the Copula-Based Regression, 136

6.4 Skew-Normal Copula, 138

6.5 Inference of Directional Dependence Using Skew-Normal Copula-Based Regression, 144

6.6 Application, 147

6.7 Conclusion, 150

7 Non-Gaussian Structural Equation Models for Causal Discovery 153
Shohei Shimizu

7.1 Introduction, 153

7.2 Independent Component Analysis, 156

7.3 Basic Linear Non-Gaussian Acyclic Model, 158

7.4 LINGAM for Time Series, 167

7.5 LINGAM with Latent Common Causes, 169

7.6 Conclusion and Future Directions, 177

8 Nonlinear Functional Causal Models for Distinguishing Cause from Effect 185
Kun Zhang and Aapo Hyvärinen

8.1 Introduction, 185

8.2 Nonlinear Additive Noise Model, 188

8.3 Post-Nonlinear Causal Model, 192

8.4 On the Relationships Between Different Principles for Model Estimation, 194

8.5 Remark on General Nonlinear Causal Models, 196

8.6 Some Empirical Results, 197

8.7 Discussion and Conclusion, 198

PARTIII GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING 203

9 Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity 205
Peter C. M. Molenaar and Lawrence L. Lo

9.1 Introduction, 205

9.2 Some Initial Remarks on the Logic of Granger Causality Testing, 206

9.3 Preliminary Introduction to Time Series Analysis