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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Guanglei Hong, University of Chicago, Department of Comparative Human Development, USA.
Inhaltsangabe
Preface xv Part I Overview 1 1 Introduction 3 1.1 Concepts of moderation, mediation, and spill-over 3 1.2 Weighting methods for causal inference 10 1.3 Objectives and organization of the book 11 1.4 How is this book situated among other publications on related topics? 12 2 Review of causal inference concepts and methods 18 2.1 Causal inference theory 18 2.2 Applications to Lord's paradox and Simpson's paradox 27 2.3 Identification and estimation 34 3 Review of causal inference designs and analytic methods 40 3.1 Experimental designs 40 3.2 Quasiexperimental designs 44 3.3 Statistical adjustment methods 46 3.4 Propensity score 55 4 Adjustment for selection bias through weighting 76 4.1 Weighted estimation of population parameters in survey sampling 77 4.2 Weighting adjustment for selection bias in causal inference 80 4.3 MMWS 86 5 Evaluations of multivalued treatments 100 5.1 Defining the causal effects of multivalued treatments 100 5.2 Existing designs and analytic methods for evaluating multivalued treatments 102 5.3 MMWS for evaluating multivalued treatments 112 5.4 Summary 123 Part II Moderation 127 6 Moderated treatment effects: concepts and existing analytic methods 129 6.1 What is moderation? 129 6.2 Experimental designs and analytic methods for investigating explicit moderators 136 6.3 Existing research designs and analytic methods for investigating implicit moderators 142 7 Marginal mean weighting through stratification for investigating moderated treatment effects 159 7.1 Existing methods for moderation analyses with quasiexperimental data 159 7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics 168 7.3 MMWS estimation of the joint effects of concurrent treatments 174 8 Cumulative effects of time-varying treatments 185 8.1 Causal effects of treatment sequences 186 8.2 Existing strategies for evaluating time-varying treatments 190 8.3 MMWS for evaluating 2-year treatment sequences 195 8.4 MMWS for evaluating multiyear sequences of multivalued treatments 204 8.5 Conclusion 207 Part III Mediation 211 9 Concepts of mediated treatment effects and experimental designs for investigating causal mechanisms 213 9.1 Introduction 214 9.2 Path coefficients 215 9.3 Potential outcomes and potential mediators 216 9.4 Causal effects with counterfactual mediators 219 9.5 Population causal parameters 222 9.6 Experimental designs for studying causal mediation 225 10 Existing analytic methods for investigating causal mediation mechanisms 238 10.1 Path analysis and SEM 239 10.2 Modified regression approach 246 10.3 Marginal structural models 250 10.4 Conditional structural models 252 10.5 Alternative weighting methods 254 10.6 Resampling approach 256 10.7 IV method 257 10.8 Principal stratification 259 10.9 Sensitivity analysis 261 10.10 Conclusion 265 11 Investigations of a simple mediation mechanism 273 11.1 Application example: national evaluation of welfare-to-work strategies 274 11.2 RMPW rationale 277 11.3 Parametric RMPW procedure 287 11.4 Nonparametric RMPW procedure 290 11.5 Simulation results 292 11.6 Discussion 295 12 RMPW extensions to alternative designs and measurement 301 12.1 RMPW extensions to mediators and outcomes of alternative distributions 301 12.2 RMPW extensions to alternative research designs 306 12.3 Alternative decomposition of the treatment effect 321 13 RMPW extensions to studies of complex mediation mechanisms 325 13.1 RMPW extensions to moderated mediation 325 13.2 RMPW extensions to concurrent mediators 328 13.3 RMPW extensions to consecutive mediators 340 13.4 Discussion 355 Part IV Spill-over 363 14 Spill-over of treatment effects: concepts and methods 365 14.1 Spill-over: A nuisance, a trifle, or a focus? 365 14.2 Stable versus unstable potential outcome values: An example from agriculture 367 14.3 Consequences for causal inference when spill-over is overlooked 369 14.4 Modified framework of causal inference 371 14.5 Identification: Challenges and solutions 376 14.6 Analytic strategies for experimental and quasiexperimental data 384 14.7 Summary 387 15 Mediation through spill-over 391 15.1 Definition of mediated effects through spill-over in a cluster randomized trial 393 15.2 Identification and estimation of the spill-over effect in a cluster randomized design 395 15.3 Definition of mediated effects through spill-over in a multisite trial 402 15.4 Identification and estimation of spill-over effects in a multisite trial 406 15.5 Consequences of omitting spill-over effects in causal mediation analyses 412 15.6 Quasiexperimental application 416 15.7 Summary 419 Index 423
Preface xv Part I Overview 1 1 Introduction 3 1.1 Concepts of moderation, mediation, and spill-over 3 1.2 Weighting methods for causal inference 10 1.3 Objectives and organization of the book 11 1.4 How is this book situated among other publications on related topics? 12 2 Review of causal inference concepts and methods 18 2.1 Causal inference theory 18 2.2 Applications to Lord's paradox and Simpson's paradox 27 2.3 Identification and estimation 34 3 Review of causal inference designs and analytic methods 40 3.1 Experimental designs 40 3.2 Quasiexperimental designs 44 3.3 Statistical adjustment methods 46 3.4 Propensity score 55 4 Adjustment for selection bias through weighting 76 4.1 Weighted estimation of population parameters in survey sampling 77 4.2 Weighting adjustment for selection bias in causal inference 80 4.3 MMWS 86 5 Evaluations of multivalued treatments 100 5.1 Defining the causal effects of multivalued treatments 100 5.2 Existing designs and analytic methods for evaluating multivalued treatments 102 5.3 MMWS for evaluating multivalued treatments 112 5.4 Summary 123 Part II Moderation 127 6 Moderated treatment effects: concepts and existing analytic methods 129 6.1 What is moderation? 129 6.2 Experimental designs and analytic methods for investigating explicit moderators 136 6.3 Existing research designs and analytic methods for investigating implicit moderators 142 7 Marginal mean weighting through stratification for investigating moderated treatment effects 159 7.1 Existing methods for moderation analyses with quasiexperimental data 159 7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics 168 7.3 MMWS estimation of the joint effects of concurrent treatments 174 8 Cumulative effects of time-varying treatments 185 8.1 Causal effects of treatment sequences 186 8.2 Existing strategies for evaluating time-varying treatments 190 8.3 MMWS for evaluating 2-year treatment sequences 195 8.4 MMWS for evaluating multiyear sequences of multivalued treatments 204 8.5 Conclusion 207 Part III Mediation 211 9 Concepts of mediated treatment effects and experimental designs for investigating causal mechanisms 213 9.1 Introduction 214 9.2 Path coefficients 215 9.3 Potential outcomes and potential mediators 216 9.4 Causal effects with counterfactual mediators 219 9.5 Population causal parameters 222 9.6 Experimental designs for studying causal mediation 225 10 Existing analytic methods for investigating causal mediation mechanisms 238 10.1 Path analysis and SEM 239 10.2 Modified regression approach 246 10.3 Marginal structural models 250 10.4 Conditional structural models 252 10.5 Alternative weighting methods 254 10.6 Resampling approach 256 10.7 IV method 257 10.8 Principal stratification 259 10.9 Sensitivity analysis 261 10.10 Conclusion 265 11 Investigations of a simple mediation mechanism 273 11.1 Application example: national evaluation of welfare-to-work strategies 274 11.2 RMPW rationale 277 11.3 Parametric RMPW procedure 287 11.4 Nonparametric RMPW procedure 290 11.5 Simulation results 292 11.6 Discussion 295 12 RMPW extensions to alternative designs and measurement 301 12.1 RMPW extensions to mediators and outcomes of alternative distributions 301 12.2 RMPW extensions to alternative research designs 306 12.3 Alternative decomposition of the treatment effect 321 13 RMPW extensions to studies of complex mediation mechanisms 325 13.1 RMPW extensions to moderated mediation 325 13.2 RMPW extensions to concurrent mediators 328 13.3 RMPW extensions to consecutive mediators 340 13.4 Discussion 355 Part IV Spill-over 363 14 Spill-over of treatment effects: concepts and methods 365 14.1 Spill-over: A nuisance, a trifle, or a focus? 365 14.2 Stable versus unstable potential outcome values: An example from agriculture 367 14.3 Consequences for causal inference when spill-over is overlooked 369 14.4 Modified framework of causal inference 371 14.5 Identification: Challenges and solutions 376 14.6 Analytic strategies for experimental and quasiexperimental data 384 14.7 Summary 387 15 Mediation through spill-over 391 15.1 Definition of mediated effects through spill-over in a cluster randomized trial 393 15.2 Identification and estimation of the spill-over effect in a cluster randomized design 395 15.3 Definition of mediated effects through spill-over in a multisite trial 402 15.4 Identification and estimation of spill-over effects in a multisite trial 406 15.5 Consequences of omitting spill-over effects in causal mediation analyses 412 15.6 Quasiexperimental application 416 15.7 Summary 419 Index 423
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