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Statistics and Causality

Methods for Applied Empirical Research
Wiley Series in Probability and Statistics, Band 2 1. Aufl.

von: Wolfgang Wiedermann, Alexander von Eye
100,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	12.05.2016
ISBN/EAN:	9781118947067
Sprache:	englisch
Anzahl Seiten:	480

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Titelbeschreibung

>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: <ul><li>New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories</li> <li> End-of-chapter bibliographies that provide references for further discussions and additional research topics</li> <li>Discussions on the use and applicability of software when appropriate</li></ul> 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.

Inhaltsverzeichnis

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 ˇ cková-Schindler, Valeriya Naumova & ˇ 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 𝓁1 and 𝓁2 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, 323 13.6.2 Software, 324 13.6.3 Analysis, 324 13.7 Conclusion, 326 14 Design- and Model-Based Analysis of Propensity Score Designs 333 Peter M. Steiner 14.1 Introduction, 333 14.2 Causal Models and Causal Estimands, 334 14.3 Design- and Model-Based Inference with Randomized Experiments, 336 14.3.1 Design-Based Formulation, 337 14.3.2 Model-Based Formulation, 338 14.4 Design- and Model-Based Inferences with PS Designs, 339 14.4.1 Propensity Score Designs, 340 14.4.2 Design- versus Model-Based Formulations of PS Designs, 344 14.4.3 Other Propensity Score Techniques, 346 14.5 Statistical Issues with PS Designs in Practice, 347 14.5.1 Choice of a Specific PS Design, 347 14.5.2 Estimation of Propensity Scores, 350 14.5.3 Estimating and Testing the Treatment Effect, 353 14.6 Discussion, 355 15 Adjustment when Covariates are Fallible 363 Steffi Pohl, Marie-Ann Sengewald & Rolf Steyer 15.1 Introduction, 363 15.2 Theoretical Framework, 364 15.2.1 Definition of Causal Effects, 365 15.2.2 Identification of Causal Effects, 366 15.2.3 Adjusting for Latent or Fallible Covariates, 367 15.3 The Impact of Measurement Error in Covariates on Causal Effect Estimation, 369 15.3.1 Theoretical Impact of One Fallible Covariate, 369 15.3.2 Investigation of the Impact of Fallible Covariates in Simulation Studies, 370 15.3.3 Investigation of the Impact of Fallible Covariates in an Empirical Study, 370 15.4 Approaches Accounting for Latent Covariates, 372 15.4.1 Latent Covariates in Propensity Score Methods, 373 15.4.2 Latent Covariates in ANCOVA Models, 374 15.4.3 Performance of the Approaches in an Empirical Study, 374 15.5 The Impact of Additional Covariates on the Biasing Effect of a Fallible Covariate, 375 15.5.1 Investigation of the Impact of Additional Covariates in an Empirical Study, 376 15.5.2 Investigation of the Impact of Additional Covariates in Simulation Studies, 378 15.6 Discussion, 379 16 Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile 385 Stephanie T. Lanza, Megan S. Schuler & Bethany C. Bray 16.1 Introduction, 385 16.2 Latent Class Analysis, 387 16.2.1 LCA With Covariates, 387 16.3 Propensity Score Analysis, 389 16.3.1 Inverse Propensity Weights (IPWs), 390 16.4 Empirical Demonstration, 391 16.4.1 The Causal Question: A Moderated Average Causal Effect, 391 16.4.2 Participants, 391 16.4.3 Measures, 391 16.4.4 Analytic Strategy for LCA With Causal Inference, 394 16.4.5 Results From Empirical Demonstration, 394 16.5 Discussion, 398 16.5.1 Limitations, 399 Part V Designs For Causal Inference 405 17 Can We Establish Causality with Statistical Analyses? The Example of Epidemiology 407 Ulrich Frick & Jürgen Rehm 17.1 Why a Chapter on Design?, 407 17.2 The Epidemiological Theory of Causality, 408 17.3 Cohort and Case-Control Studies, 411 17.4 Improving Control in Epidemiological Research, 414 17.4.1 Measurement, 414 17.4.2 Mendelian Randomization, 416 17.4.3 Surrogate Endpoints (Experimental), 419 17.4.4 Other Design Measures to Increase Control, 420 17.4.5 Methods of Analysis, 421 17.5 Conclusion: Control in Epidemiological Research Can Be Improved, 424 Index 433 <div id="_mcePaste" style="position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;"> 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 ˇ cková-Schindler, Valeriya Naumova & ˇ 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 𝓁1 and 𝓁2 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, 323 13.6.2 Software, 324 13.6.3 Analysis, 324 13.7 Conclusion, 326 14 Design- and Model-Based Analysis of Propensity Score Designs 333 Peter M. Steiner 14.1 Introduction, 333 14.2 Causal Models and Causal Estimands, 334 14.3 Design- and Model-Based Inference with Randomized Experiments, 336 14.3.1 Design-Based Formulation, 337 14.3.2 Model-Based Formulation, 338 14.4 Design- and Model-Based Inferences with PS Designs, 339 14.4.1 Propensity Score Designs, 340 14.4.2 Design- versus Model-Based Formulations of PS Designs, 344 14.4.3 Other Propensity Score Techniques, 346 14.5 Statistical Issues with PS Designs in Practice, 347 14.5.1 Choice of a Specific PS Design, 347 14.5.2 Estimation of Propensity Scores, 350 14.5.3 Estimating and Testing the Treatment Effect, 353 14.6 Discussion, 355 15 Adjustment when Covariates are Fallible 363 Steffi Pohl, Marie-Ann Sengewald & Rolf Steyer 15.1 Introduction, 363 15.2 Theoretical Framework, 364 15.2.1 Definition of Causal Effects, 365 15.2.2 Identification of Causal Effects, 366 15.2.3 Adjusting for Latent or Fallible Covariates, 367 15.3 The Impact of Measurement Error in Covariates on Causal Effect Estimation, 369 15.3.1 Theoretical Impact of One Fallible Covariate, 369 15.3.2 Investigation of the Impact of Fallible Covariates in Simulation Studies, 370 15.3.3 Investigation of the Impact of Fallible Covariates in an Empirical Study, 370 15.4 Approaches Accounting for Latent Covariates, 372 15.4.1 Latent Covariates in Propensity Score Methods, 373 15.4.2 Latent Covariates in ANCOVA Models, 374 15.4.3 Performance of the Approaches in an Empirical Study, 374 15.5 The Impact of Additional Covariates on the Biasing Effect of a Fallible Covariate, 375 15.5.1 Investigation of the Impact of Additional Covariates in an Empirical Study, 376 15.5.2 Investigation of the Impact of Additional Covariates in Simulation Studies, 378 15.6 Discussion, 379 16 Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile 385 Stephanie T. Lanza, Megan S. Schuler & Bethany C. Bray 16.1 Introduction, 385 16.2 Latent Class Analysis, 387 16.2.1 LCA With Covariates, 387 16.3 Propensity Score Analysis, 389 16.3.1 Inverse Propensity Weights (IPWs), 390 16.4 Empirical Demonstration, 391 16.4.1 The Causal Question: A Moderated Average Causal Effect, 391 16.4.2 Participants, 391 16.4.3 Measures, 391 16.4.4 Analytic Strategy for LCA With Causal Inference, 394 16.4.5 Results From Empirical Demonstration, 394 16.5 Discussion, 398 16.5.1 Limitations, 399 Part V Designs For Causal Inference 405 17 Can We Establish Causality with Statistical Analyses? The Example of Epidemiology 407 Ulrich Frick & Jürgen Rehm 17.1 Why a Chapter on Design?, 407 17.2 The Epidemiological Theory of Causality, 408 17.3 Cohort and Case-Control Studies, 411 17.4 Improving Control in Epidemiological Research, 414 17.4.1 Measurement, 414 17.4.2 Mendelian Randomization, 416 17.4.3 Surrogate Endpoints (Experimental), 419 17.4.4 Other Design Measures to Increase Control, 420 17.4.5 Methods of Analysis, 421 17.5 Conclusion: Control in Epidemiological Research Can Be Improved, 424 Index 433 </div>

Autorenportrait

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.

Back cover copy

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: <ul><li>New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories</li> <li> End-of-chapter bibliographies that provide references for further discussions and additional research topics</li> <li>Discussions on the use and applicability of software when appropriate</li></ul> 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.