Description
Book SynopsisCausality 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.
Table of ContentsPreface 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
