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Causality in a Social World: Moderation, Mediation and Spill-over

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<p>Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data.The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory.Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.</p><br/><p>Preface xvPart I Overview 11 Introduction 31.1 Concepts of moderation, mediation, and spill-over 31.1.1 Moderated treatment effects 51.1.2 Mediated treatment effects 71.1.3 Spill-over effects of a treatment 81.2 Weighting methods for causal inference 101.3 Objectives and organization of the book 111.4 How is this book situated among other publications on related topics? 122 Review of causal inference concepts and methods 182.1 Causal inference theory 182.1.1 Attributes versus causes 182.1.2 Potential outcomes and individual-specific causal effects 192.1.3 Inference about population average causal effects 222.2 Applications to Lord's paradox and Simpson's paradox 272.2.1 Lord's paradox 272.2.2 Simpson's paradox 312.3 Identification and estimation 342.3.1 Selection bias 352.3.2 Sampling bias 352.3.3 Estimation efficiency 36Appendix 2.1: Potential bias in a prima facie effect 36Appendix 2.2: Application of the causal inference theory to Lord's paradox 373 Review of causal inference designs and analytic methods 403.1 Experimental designs 403.1.1 Completely randomized designs 403.1.2 Randomized block designs 413.1.3 Covariance adjustment for improving efficiency 433.1.4 Multilevel experimental designs 433.2 Quasiexperimental designs 443.2.1 Nonequivalent comparison group designs 443.2.2 Other quasiexperimental designs 453.3 Statistical adjustment methods 463.3.1 ANCOVA and multiple regression 463.3.2 Matching and stratification 503.3.3 Other statistical adjustment methods 513.4 Propensity score 553.4.1 What is a propensity score? 563.4.2 Balancing property of the propensity score 573.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment 60Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interaction 70Appendix 3.B: Variable selection for the propensity score model 714 Adjustment for selection bias through weighting 764.1 Weighted estimation of population parameters in survey sampling 774.1.1 Simple random sample 774.1.2 Proportionate sample 784.1.3 Disproportionate sample 794.2 Weighting adjustment for selection bias in causal inference 804.2.1 Experimental result 814.2.2 Quasiexperimental result 814.2.3 Sample weight for bias removal 824.2.4 IPTW for bias removal 844.3 MMWS 864.3.1 Theoretical rationale 864.3.2 MMWS analytic procedure 914.3.3 Inherent connection and major distinctions between MMWS and IPTW 93Appendix 4.A: Proof of MMWS-adjusted mean observed outcome being unbiased for the population average potential outcome 95Appendix 4.B: Derivation of MMWS for estimating the treatment effect on the treated 96Appendix 4.C: Theoretical equivalence of MMWS and IPTW 97Appendix 4.D: Simulations comparing MMWS and IPTW under misspecifications of the functional form of a propensity score model 975 Evaluations of multivalued treatments 1005.1 Defining the causal effects of multivalued

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