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

ISBN-13: 9781118332566 / Angielski / Twarda / 2015 / 448 str.

Guanglei Hong

Opis

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.

Spis treści

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

O autorze

Guanglei Hong, University of Chicago, Department of Comparative Human Development, USA.

Blurb

Innovative strategies for the empirical study of causal effects for social scientists

Causality in a Social World introduces a series of weighting methods, often nonparametric in nature, for addressing a wide range of causal questions while minimizing the reliance on model–based assumptions. Rather than fitting research questions to existing statistical tools regardless of their constraining assumptions, this book offers numerous application examples in which causal inquiry is driven by scientific and practical interests. 

Key features:

• Demonstrates how each causal relationship can be defined in terms of potential outcomes and identified using optimal experimental designs.
• Compares the identification assumptions invoked by various statistical techniques for analyses of non–experimental data. 

• Surveys existing approaches along with presenting innovative new methodologies for causal analysis of moderation, mediation, and spill–over effects with examples from social scientific research.
• Provides detailed descriptions of the weighting procedures.
• Offers user–friendly software packages as well as syntax for the commonly used statistical programs SPSS, SAS, Stata and R.

Causality in a Social World provides guidance to social scientists, graduate and post–graduate students in their empirical investigations of causality.

Researchers conducting policy analysis and program evaluation in research companies or in research offices at different levels of the government will also benefit from this book.