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Propensity Score Analysis

With a strong focus on practical applications, the authors explore various strategies for employing PSA. In addition, they discuss the use of PSA with alternative types of data and limitations of PSA under a variety of constraints. Unlike the existing textbooks on program evaluation and causal inference, Propensity Score Analysis delves into statistical concepts, formulas, and models in the context of a robust and engaging focus on application.
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Table of Contents

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 Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index

About the Author

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. Mark W. Fraser, PhD, holds the Tate Distinguished Professorship at the School of Social Work, University of North Carolina where he serves as associate dean for research. He has won numerous awards for research and teaching, including the Aaron Rosen Award and the Distinguished Achievement Award from the Society for Social Work and Research. His work focuses on risk and resilience, child behavior, child and family services, and research methods. Dr. Fraser has published widely, and, in addition to Social Policy for Children and Families, is the co-author or editor of eight books. These include Families in Crisis, a study of intensive family-centered services, and Evaluating Family-Based Services, a text on methods for family research. In Risk and Resilience in Childhood, he and his colleagues describe resilience-based perspectives for child maltreatment, substance abuse, and other social problems. In Making Choices, Dr. Fraser and his co-authors outline a program to help children build sustaining social relationships. In The Context of Youth Violence, he explores violence from the perspective of resilience, risk, and protection, and in Intervention with Children and Adolescents, Dr. Fraser and his colleagues review advances in intervention knowledge for social and health problems. Intervention Research: Developing Social Programs describes the design and development of social programs. His most recent book is Propensity Score Analysis: Statistical Methods and Applications. Dr. Fraser serves as editor of the Journal of the Society for Social Work and Research. He is a fellow of the National Academies of Practice and the American Academy of Social Work and Social Welfare.


Over the past 35 years, methods of program evaluation have undergone a significant change, and the researchers have recognized the need to develop more efficient approaches for assessing treatment effects from studies based on observational data and for evaluations based on quasi-experimental designs. Written by experts, this volume is updated and fully reflects the current changes to the field. It offers a systematic review of the history, origins, and statistical foundations of propensity score analysis, and more. -- NeoPopRealism Journal

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