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

Statistics and Causality: Methods for Applied Empirical Research

LIST OF CONTRIBUTORS xiiiPREFACE xviiACKNOWLEDGMENTS xxvPART I BASES OF CAUSALITY 11 Causation and the Aims of Inquiry 3Ned Hall1.1 Introduction, 31.2 The Aim of an Account of Causation, 41.3 The Good News, 71.4 The Challenging News, 171.5 The Perplexing News, 262 Evidence and Epistemic Causality 31Michael Wilde and Jon Williamson2.1 Causality and Evidence, 312.2 The Epistemic Theory of Causality, 352.3 The Nature of Evidence, 382.4 Conclusion, 40PART II DIRECTIONALITY OF EFFECTS 433 Statistical Inference for Direction of Dependence in Linear Models 45Yadolah Dodge and Valentin Rousson3.1 Introduction, 453.2 Choosing the Direction of a Regression Line, 463.3 Significance Testing for the Direction of a Regression Line, 483.4 Lurking Variables and Causality, 543.5 Brain and Body Data Revisited, 573.6 Conclusions, 604 Directionality of Effects in Causal Mediation Analysis 63Wolfgang Wiedermann and Alexander von Eye4.1 Introduction, 634.2 Elements of Causal Mediation Analysis, 664.3 Directionality of Effects in Mediation Models, 684.4 Testing Directionality Using Independence Properties of Competing Mediation Models, 714.5 Simulating the Performance of Directionality Tests, 824.6 Empirical Data Example: Development of Numerical Cognition, 854.7 Discussion, 925 Direction of Effects in Categorical Variables: A Structural Perspective 107Alexander von Eye and Wolfgang Wiedermann5.1 Introduction, 1075.2 Concepts of Independence in Categorical Data Analysis, 1085.3 Direction Dependence in Bivariate Settings: Metric and Categorical Variables, 1105.4 Explaining the Structure of Cross-Classifications, 1175.5 Data Example, 1235.6 Discussion, 1266 Directional Dependence Analysis Using Skew-Normal Copula-Based Regression 131Seongyong Kim and Daeyoung Kim6.1 Introduction, 1316.2 Copula-Based Regression, 1336.3 Directional Dependence in the Copula-Based Regression, 1366.4 Skew-Normal Copula, 1386.5 Inference of Directional Dependence Using Skew-Normal Copula-Based Regression, 1446.6 Application, 1476.7 Conclusion, 1507 Non-Gaussian Structural Equation Models for Causal Discovery 153Shohei Shimizu7.1 Introduction, 1537.2 Independent Component Analysis, 1567.3 Basic Linear Non-Gaussian Acyclic Model, 1587.4 LINGAM for Time Series, 1677.5 LINGAM with Latent Common Causes, 1697.6 Conclusion and Future Directions, 1778 Nonlinear Functional Causal Models for Distinguishing Cause from Effect 185Kun Zhang and Aapo Hyvärinen8.1 Introduction, 1858.2 Nonlinear Additive Noise Model, 1888.3 Post-Nonlinear Causal Model, 1928.4 On the Relationships Between Different Principles for Model Estimation, 1948.5 Remark on General Nonlinear Causal Models, 1968.6 Some Empirical Results, 1978.7 Discussion and Conclusion, 198PARTIII GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING 2039 Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity 205Peter C. M. Molenaar and Lawrence L. Lo9.1 Introduction, 2059.2 Some Initial Remarks on the Logic of Granger Causality Testing, 2069.3 Preliminary Introduction to Time Series Analysis, 2079.4 Overview of Granger Causality Testing in the Time Domain, 2109.5 Granger Causality Testing in the Frequency Domain, 2129.6 A New Data-Driven Solution to Granger Causality Testing, 2169.7 Extensions to Nonstationary Series and Heterogeneous Replications, 2219.8 Discussion and Conclusion, 22410 Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models 231Ingrid Koller, Claus H. Carstensen, Wolfgang Wiedermann and Alexander von Eye10.1 Introduction, 23110.2 Granger Causation, 23210.3 The Rasch Model, 23410.4 Longitudinal Item Response Theory Models, 23610.5 Data Example: Scientific Literacy in Preschool Children, 24010.6 Discussion, 24111 Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences 249Katrina Hlav ková-Schindler, Valeriya Naumova and Sergiy Pereverzyev Jr.11.1 Introduction, 24911.2 Granger Causality and Multivariate Granger Causality, 25111.3 Gene Regulatory Networks, 25411.4 Regularization of Ill-Posed Inverse Problems, 25511.5 Multivariate Granger Causality Approaches Using l1 and l2 Penalties, 25611.6 Applied Quality Measures, 26211.7 Novel Regularization Techniques with a Case Study of Gene Regulatory Networks Reconstruction, 26311.8 Conclusion, 27112 Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models 277Phillip K. Wood12.1 Introduction, 27712.2 Types of Reciprocal Relationship Models, 27812.3 Unmeasured Reciprocal and Autocausal Effects, 28612.4 Longitudinal Data Settings, 29312.5 Discussion, 304PARTIV COUNTERFACTUAL APPROACHES AND PROPENSITY SCORE ANALYSIS 30913 Log-Linear Causal Analysis of Cross-Classified Categorical Data 311Kazuo Yamaguchi13.1 Introduction, 31113.2 Propensity Score Methods and the Collapsibility Problem for the Logit Model, 31313.3 Theorem On Standardization and the Lack of Collapsibility of the Logit Model, 31613.4 The Problem of Zero-Sample Estimates of Conditional Probabilities and the Use of Semiparametric Models to Solve the Problem, 31813.5 Estimation of Standard Errors in the Analysis of Association with Adjusted Contingency Table Data, 32213.6 Illustrative Application, 32313.7 Conclusion, 32614 Design- and Model-Based Analysis of Propensity Score Designs 333Peter M. Steiner14.1 Introduction, 33314.2 Causal Models and Causal Estimands, 33414.3 Design- and Model-Based Inference with Randomized Experiments, 33614.4 Design- and Model-Based Inferences with PS Designs, 33914.5 Statistical Issues with PS Designs in Practice, 34714.6 Discussion, 35515 Adjustment when Covariates are Fallible 363Steffi Pohl, Marie-Ann Sengewald and Rolf Steyer15.1 Introduction, 36315.2 Theoretical Framework, 36415.3 The Impact of Measurement Error in Covariates on Causal Effect Estimation, 36915.4 Approaches Accounting for Latent Covariates, 37215.5 The Impact of Additional Covariates on the Biasing Effect of a Fallible Covariate, 37515.6 Discussion, 37916 Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile 385Stephanie T. Lanza, Megan S. Schuler and Bethany C. Bray16.1 Introduction, 38516.2 Latent Class Analysis, 38716.3 Propensity Score Analysis, 38916.4 Empirical Demonstration, 39116.5 Discussion, 39816.5.1 Limitations, 399PART V DESIGNS FOR CAUSAL INFERENCE 40517 Can We Establish Causality with Statistical Analyses? The Example of Epidemiology 407Ulrich Frick and Jürgen Rehm17.1 Why a Chapter on Design?, 40717.2 The Epidemiological Theory of Causality, 40817.3 Cohort and Case-Control Studies, 41117.4 Improving Control in Epidemiological Research, 41417.5 Conclusion: Control in Epidemiological Research Can BeImproved, 424INDEX 433