Buch, Englisch, 480 Seiten, Format (B × H): 155 mm x 236 mm, Gewicht: 794 g
Methods for Applied Empirical Research
Buch, Englisch, 480 Seiten, Format (B × H): 155 mm x 236 mm, Gewicht: 794 g
Reihe: Wiley Series in Probability and Statistics
ISBN: 978-1-118-94704-3
Verlag: Wiley
>STATISTICS AND CAUSALITY
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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
1.2.1 The Possible Utility of a False Account, 4
1.2.2 Inquiry’s Aim, 5
1.2.3 Role of “Intuitions”, 6
1.3 The Good News, 7
1.3.1 The Core Idea, 7
1.3.2 Taxonomizing “Conditions”, 9
1.3.3 Unpacking “Dependence”, 10
1.3.4 The Good News, Amplified, 12
1.4 The Challenging News, 17
1.4.1 Multiple Realizability, 17
1.4.2 Protracted Causes, 18
1.4.3 Higher Level Taxonomies and “Normal” Conditions, 25
1.5 The Perplexing News, 26
1.5.1 The Centrality of “Causal Process”, 26
1.5.2 A Speculative Proposal, 28
2 Evidence and Epistemic Causality 31
Michael Wilde & 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 & 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
3.4.1 Two Independent Predictors, 55
3.4.2 Confounding Variable, 55
3.4.3 Selection of a Subpopulation, 56
3.5 Brain and Body Data Revisited, 57
3.6 Conclusions, 60
4 Directionality of Effects in Causal Mediation Analysis 63
Wolfgang Wiedermann & 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
4.4.1 Independence Properties of Bivariate Relations, 72
4.4.2 Independence Properties of the Multiple Variable Model, 74
4.4.3 Measuring and Testing Independence, 74
4.5 Simulating the Performance of Directionality Tests, 82
4.5.1 Results, 83
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 & 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
5.3.1 Simulating the Performance of Nonhierarchical Log-Linear Models, 114
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 & Daeyoung Kim
6.1 Introduction, 131
6.2 Copula-Based Regression, 133
6.2.1 Copula, 133
6.2.2 Copula-Based Regression, 134
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
6.5.1 Estimation of Copula-Based Regression, 144
6.5.2 Detection of Directional Dependence and Computation of the Directional Dependence Measures, 146
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
7.2.1 Model, 157
7.2.2 Identifiability, 157
7.2.3 Estimation, 158
7.3 Basic Linear Non-Gaussian Acyclic Model, 158
7.3.1 Model, 158
7.3.2 Identifiability, 160
7.3.3 Estimation, 162
7.4 LINGAM for Time Series, 167
7.4.1 Model, 167
7.4.2 Identifiability, 168
7.4.3 Estimation, 168
7.5 LINGAM with Latent Common Causes, 169
7.5.1 Model, 169
7.5.2 Identifiability, 171
7.5.3 Estimation, 174
7.6 Conclusion and Future Directions, 177
8 Nonlinear Functional Causal Models for Distinguishing Cause from Effect 185
Kun Zhang & Aapo Hyvärinen
8.1 Introduction, 185
8.2 Nonlinear Additive Noise Model, 188
8.2.1 Definition of Model, 188
8.2.2 Likelihood Ratio for Nonlinear Additive Models, 188
8.2.3 Information-Theoretic Interpretation, 189
8.2.4 Likelihood Ratio and Independence-Based Methods, 191
8.3 Post-Nonlinear Causal Model, 192
8.3.1 The Model, 192
8.3.2 Identifiability of Causal Direction, 193
8.3.3 Determination of Causal Direction Based on the PNL Causal Model, 193
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
Part III Granger Causality And Longitudinal Data Modeling 203
9 Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity 205
Peter C. M. Molenaar & 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, 207
9.4 Overview of Granger Causality Testing in the Time Domain, 210
9.5 Granger Causality Testing in the Frequency Domain, 212
9.5.1 Two Equivalent Representations of a VAR(a), 212
9.5.2 Partial Directed Coherence (PDC) as a Frequency-Domain Index of Granger Causality, 213
9.5.3 Some Preliminary Comments, 214
9.5.4 Application to Simulated Data, 215
9.6 A New Data-Driven Solution to Granger Causality Testing, 216
9.6.1 Fitting a uSEM, 217
9.6.
