Practical Propensity Score Methods Using R

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

Review :
"This book offers a comprehensive, accessible, and timely treatment of propensity score analysis and its application for estimating treatment effects from observational data with varying levels of complexity. Both novice and advanced users of this methodology will appreciate the breadth and depth of the practical knowledge that Walter Leite offers, and the useful examples he provides." "Clearly written and technically sound, this text should be a staple for researchers and methodologists alike. Not only is the text an excellent resource for understanding propensity score analysis, but the author has recognized the messiness of real data, and helps the reader understand and appropriately address issues such as missing data and complex samples. This is extremely refreshing." "This book provides an overview of propensity score analysis. The author’s introduction situates propensity score analysis within Rubin’s Causal Model and Campbell’s Framework. This text will be good for the advanced user with previous knowledge of the R language, complex survey design, and missing data." "This book provides an excellent definition of propensity scores and the sequential steps required in its application." "It is a well-crafted practical book on propensity score methods and features the free software R. I believe many students will like it." "With the use of examples consisting of real survey data, Practical Propensity Score Methods Using R provides a wide range of detailed information on how to reduce bias in research studies that seek to test treatment effects in situations where random assignment was not implemented." In general, the book is well-crafted and focuses on practical implementation of propensity score methods featuring the free software R. Even though there is room for improvement that could be addressed in a second edition, we believe that it is a useful book for researchers and graduate students, and therefore, many readers will find it beneficial.