Propensity Score Analysis

List of Tables
List of Figures
Preface
About the Authors
Chapter 1: Introduction
Observational Studies
History and Development
Randomized Experiments
Why and When a Propensity Score Analysis Is Needed
Computing Software Packages
Plan of the Book
Chapter 2: Counterfactual Framework and Assumptions
Causality, Internal Validity, and Threats
Counterfactuals and the Neyman-Rubin Counterfactual Framework
The Ignorable Treatment Assignment Assumption
The Stable Unit Treatment Value Assumption
Methods for Estimating Treatment Effects
The Underlying Logic of Statistical Inference
Types of Treatment Effects
Treatment Effect Heterogeneity
Heckman's Econometric Model of Causality
Conclusion
Chapter 3: Conventional Methods for Data Balancing
Why Is Data Balancing Necessary? A Heuristic Example
Three Methods for Data Balancing
Design of the Data Simulation
Results of the Data Simulation
Implications of the Data Simulation
Key Issues Regarding the Application of OLS Regression
Conclusion
Chapter 4: Sample Selection and Related Models
The Sample Selection Model
Treatment Effect Model
Overview of the Stata Programs and Main Features of treatreg
Examples
Conclusion
Chapter 5: Propensity Score Matching and Related Models
Overview
The Problem of Dimensionality and the Properties of Propensity Scores
Estimating Propensity Scores
Matching
Postmatching Analysis
Propensity Score Matching With Multilevel Data
Overview of the Stata and R Programs
Examples
Conclusion
Chapter 6: Propensity Score Subclassification
Overview
The Overlap Assumption and Methods to Address Its Violation
Structural Equation Modeling With Propensity Score Subclassification
The Stratification-Multilevel Method
Examples
Conclusion
Chapter 7: Propensity Score Weighting
Overview
Weighting Estimators
Examples
Conclusion
Chapter 8: Matching Estimators
Overview
Methods of Matching Estimators
Overview of the Stata

Shenyang Guo, PhD, is the Kuralt Distinguished Professor at the School of Social Work, University of North Carolina. The author of numerous articles on statistical methods and research reports in child welfare, child mental health services, welfare, and health care, Guo has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, and program evaluation. He has given many invited workshops on statistical methods-including event history analysis and propensity score matching-at the NIH Summer Institute, Children's Bureau, and at conferences of the Society of Social Work and Research. He led the data analysis planning for the National Survey of Child and Adolescent Well-Being (NSCAW) longitudinal analysis.