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Wolfgang Wiedermann og Alexander von Eye
(2016)
John Wiley & Sons
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Statistics and Causality
Methods for Applied Empirical Research
Wolfgang Wiedermann og Alexander von Eye
(2016)
Sprog: Engelsk
John Wiley & Sons, Incorporated
1.268,00 kr.
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Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: John Wiley & Sons (Maj 2016)
- Forfattere: Wolfgang Wiedermann og Alexander von Eye
- ISBN: 9781118947067
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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Detaljer om varen
- Hardback: 480 sider
- Udgiver: John Wiley & Sons, Incorporated (Juni 2016)
- Forfattere: Wolfgang Wiedermann og Alexander von Eye
- ISBN: 9781118947043
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 section 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.
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
9.6.2 Extending the Fit of a uSEM, 217
9.6.3 Application of the Hybrid VAR Fit to Simulated Data, 218
9.7 Extensions to Nonstationary Series and Heterogeneous Replications, 221
9.7.1 Heterogeneous Replications, 221
9.7.2 Nonstationary Series, 222
9.8 Discussion and Conclusion, 224 10 Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models 231 Ingrid Koller, Claus H. Carstensen, Wolfgang Wiedermann & Alexander von Eye
10.1 Introduction, 231
10.2 Granger Causation, 232
10.3 The Rasch Model, 234
10.4 Longitudinal Item Response Theory Models, 236
10.5 Data Example: Scientific Literacy in Preschool Children, 240
10.6 Discussion, 241 11 Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences 249 Katerina Hlavá cková-Schindler, Valeriya Naumova & Sergiy Pereverzyev Jr.
11.1 Introduction, 249
11.1.1 Causality Problems in Life Sciences, 250
11.1.2 Outline of the
Chapter, 250
11.1.3 Notation, 251
11.2 Granger Causality and Multivariate Granger Causality, 251
11.2.1 Granger Causality, 252
11.2.2 Multivariate Granger Causality, 253
11.3 Gene Regulatory Networks, 254
11.4 Regularization of Ill-Posed Inverse Problems, 255
11.5 Multivariate Granger Causality Approaches Using 1 and 2 Penalties, 256
11.6 Applied Quality Measures, 262
11.7 Novel Regularization Techniques with a Case Study of Gene Regulatory Networks Reconstruction, 263
11.7.1 Optimal Graphical Lasso Granger Estimator, 263
11.7.2 Thresholding Strategy, 264
11.7.3 An Automatic Realization of the GLG-Method, 266
11.7.4 Granger Causality with Multi-Penalty Regularization, 266
11.7.5 Case Study of Gene Regulatory Network Reconstruction, 269
11.8 Conclusion, 271 12 Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models 277 Phillip K. Wood
12.1 Introduction, 277
12.2 Types of Reciprocal Relationship Models, 278
12.2.1 Cross-Lagged Panel Approaches, 278
12.2.2 Granger Causality, 279
12.2.3 Epistemic Causality, 280
12.2.4 Reciprocal Causality, 281
12.3 Unmeasured Reciprocal and Autocausal Effects, 286
12.3.1 Bias in Standardized Regression Weight, 288
12.3.2 Autocausal Effects, 289
12.3.3 Instrumental Variables, 291
12.4 Longitudinal Data Settings, 293
12.4.1 Monte Carlo Simulation, 293
12.4.2 Real-World Data Examples, 302
12.5 Discussion, 304
Part IV Counterfactual Approaches And Propensity Score Analysis 309 13 Log-Linear Causal Analysis of Cross-Classified Categorical Data 311 Kazuo Yamaguchi
13.1 Introduction, 311
13.2 Propensity Score Methods and the Collapsibility Problem for the Logit Model, 313
13.3 Theorem On Standardization and the Lack of Collapsibility of the Logit Model, 316
13.4 The Problem of Zero-Sample Estimates of Conditional Probabilities and the Use of Semiparametric Models to Solve the Problem, 318
13.4.1 The Problem of Zero-Sample Estimates of Conditional Probabilities, 318
13.4.2 Method for Obtaining Adjusted Two-Way Frequency Data for the Analysis of Association between X and Y, 319
13.4.3 Method for Obtaining an Adjusted Three-Way Frequency Table for the Analysis of Conditional Association, 320
13.5 Estimation of Standard Errors in the Analysis of Association with Adjusted Contingency Table Data, 322
13.6 Illustrative Application, 323
13.6.1 Data, 3
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
9.6.2 Extending the Fit of a uSEM, 217
9.6.3 Application of the Hybrid VAR Fit to Simulated Data, 218
9.7 Extensions to Nonstationary Series and Heterogeneous Replications, 221
9.7.1 Heterogeneous Replications, 221
9.7.2 Nonstationary Series, 222
9.8 Discussion and Conclusion, 224 10 Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models 231 Ingrid Koller, Claus H. Carstensen, Wolfgang Wiedermann & Alexander von Eye
10.1 Introduction, 231
10.2 Granger Causation, 232
10.3 The Rasch Model, 234
10.4 Longitudinal Item Response Theory Models, 236
10.5 Data Example: Scientific Literacy in Preschool Children, 240
10.6 Discussion, 241 11 Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences 249 Katerina Hlavá cková-Schindler, Valeriya Naumova & Sergiy Pereverzyev Jr.
11.1 Introduction, 249
11.1.1 Causality Problems in Life Sciences, 250
11.1.2 Outline of the
Chapter, 250
11.1.3 Notation, 251
11.2 Granger Causality and Multivariate Granger Causality, 251
11.2.1 Granger Causality, 252
11.2.2 Multivariate Granger Causality, 253
11.3 Gene Regulatory Networks, 254
11.4 Regularization of Ill-Posed Inverse Problems, 255
11.5 Multivariate Granger Causality Approaches Using 1 and 2 Penalties, 256
11.6 Applied Quality Measures, 262
11.7 Novel Regularization Techniques with a Case Study of Gene Regulatory Networks Reconstruction, 263
11.7.1 Optimal Graphical Lasso Granger Estimator, 263
11.7.2 Thresholding Strategy, 264
11.7.3 An Automatic Realization of the GLG-Method, 266
11.7.4 Granger Causality with Multi-Penalty Regularization, 266
11.7.5 Case Study of Gene Regulatory Network Reconstruction, 269
11.8 Conclusion, 271 12 Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models 277 Phillip K. Wood
12.1 Introduction, 277
12.2 Types of Reciprocal Relationship Models, 278
12.2.1 Cross-Lagged Panel Approaches, 278
12.2.2 Granger Causality, 279
12.2.3 Epistemic Causality, 280
12.2.4 Reciprocal Causality, 281
12.3 Unmeasured Reciprocal and Autocausal Effects, 286
12.3.1 Bias in Standardized Regression Weight, 288
12.3.2 Autocausal Effects, 289
12.3.3 Instrumental Variables, 291
12.4 Longitudinal Data Settings, 293
12.4.1 Monte Carlo Simulation, 293
12.4.2 Real-World Data Examples, 302
12.5 Discussion, 304
Part IV Counterfactual Approaches And Propensity Score Analysis 309 13 Log-Linear Causal Analysis of Cross-Classified Categorical Data 311 Kazuo Yamaguchi
13.1 Introduction, 311
13.2 Propensity Score Methods and the Collapsibility Problem for the Logit Model, 313
13.3 Theorem On Standardization and the Lack of Collapsibility of the Logit Model, 316
13.4 The Problem of Zero-Sample Estimates of Conditional Probabilities and the Use of Semiparametric Models to Solve the Problem, 318
13.4.1 The Problem of Zero-Sample Estimates of Conditional Probabilities, 318
13.4.2 Method for Obtaining Adjusted Two-Way Frequency Data for the Analysis of Association between X and Y, 319
13.4.3 Method for Obtaining an Adjusted Three-Way Frequency Table for the Analysis of Conditional Association, 320
13.5 Estimation of Standard Errors in the Analysis of Association with Adjusted Contingency Table Data, 322
13.6 Illustrative Application, 323
13.6.1 Data, 3
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