This practical book uses a step-by-step analysis of realistic examples to help students understand the theory and code for implementing propensity score analysis with the R statistical language. With a comparison of both well-established and cutting-edge propensity score methods, the text highlights where solid guidelines exist to support best practices and where there is scarcity of research. Readers will find that this scaffolded approach to R and the book’s free online resources help them apply the text’s concepts to the analysis of their own data. 

Table of Contents:
Preface Acknowledgments About the Author Chapter 1. Overview of Propensity Score Analysis Learning Objectives 1.1 Introduction 1.2 Rubin’s Causal Model 1.3 Campbell’s Framework 1.4 Propensity Scores 1.5 Description of Example 1.6 Steps of Propensity Score Analysis 1.7 Propensity Score Analysis With Complex Survey Data 1.8 Resources for Learning R 1.9 Conclusion Study Questions Chapter 2. Propensity Score Estimation Learning Objectives 2.1 Introduction 2.2 Description of Example 2.3 Selection of Covariates 2.4 Dealing With Missing Data 2.5 Methods for Propensity Score Estimation 2.6 Evaluation of Common Support 2.7 Conclusion Study Questions Chapter 3. Propensity Score Weighting Learning Objectives 3.1 Introduction 3.2 Description of Example 3.3 Calculation of Weights 3.4 Covariate Balance Check 3.5 Estimation of Treatment Effects With Propensity Score Weighting 3.6 Propensity Score Weighting With Multiple Imputed Data Sets 3.7 Doubly Robust Estimation of Treatment Effect With Propensity Score Weighting 3.8 Sensitivity Analysis 3.9 Conclusion Study Questions Chapter 4. Propensity Score Stratification Learning Objectives 4.1 Introduction 4.2 Description of Example 4.3 Propensity Score Estimation 4.4 Propensity Score Stratification 4.5 Marginal Mean Weighting Through Stratification 4.6 Conclusion Study Questions Chapter 5. Propensity Score Matching Learning Objectives 5.1 Introduction 5.2 Description of Example 5.3 Propensity Score Estimation 5.4 Propensity Score Matching Algorithms 5.5 Evaluation of Covariate Balance 5.6 Estimation of Treatment Effects 5.7 Sensitivity Analysis 5.8 Conclusion Study Questions Chapter 6. Propensity Score Methods for Multiple Treatments Learning Objectives 6.1 Introduction 6.2 Description of Example 6.3 Estimation of Generalized Propensity Scores With Multinomial Logistic Regression 6.4 Estimation of Generalized Propensity Scores With Data Mining Methods 6.5 Propensity Score Weighting for Multiple Treatments 6.6 Estimation of Treatment Effect of Multiple Treatments 6.7 Conclusion Study Questions Chapter 7. Propensity Score Methods for Continuous Treatment Doses Learning Objectives 7.1 Introduction 7.2 Description of Example 7.3 Generalized Propensity Scores 7.4 Inverse Probability Weighting 7.5 Conclusion Study Questions Chapter 8. Propensity Score Analysis With Structural Equation Models Learning Objectives 8.1 Introduction 8.2 Description of Example 8.3 Latent Confounding Variables 8.4 Estimation of Propensity Scores 8.5 Propensity Score Methods 8.6 Treatment Effect Estimation With Multiple-Group Structural Equation Models 8.7 Treatment Effect Estimation With Multiple-Indicator and Multiple-Causes Models 8.8 Conclusion Study Questions Chapter 9. Weighting Methods for Time-Varying Treatments Learning Objectives 9.1 Introduction 9.2 Description of Example 9.3 Inverse Probability of Treatment Weights 9.4 Stabilized Inverse Probability of Treatment Weights 9.5 Evaluation of Covariate Balance 9.6 Estimation of Treatment Effects 9.7 Conclusion Study Questions Chapter 10. Propensity Score Methods With Multilevel Data Learning Objectives 10.1 Introduction 10.2 Description of Example 10.3 Estimation of Propensity Scores With Multilevel Data 10.4 Propensity Score Weighting 10.5 Treatment Effect Estimation 10.6 Conclusion Study Questions References Index