Propensity score analysis: statistical methods and applications

Propensity Score Matching provides readers with a systematic review of the origins, history, and statistical foundations of PSM and illustrates how to use PSM methods for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems, strategies for using the methods, and the limitations of PSM. Unlike the existing textbooks on program evaluation, Guo and Fraser’s Propensity Score Matching delves into statistical concepts, formulas, and models underlying the application of PSM. INDICE: INTRODUCTION / Observational Studies / History and Development / Randomized Experiment / Why and When the Propensity Score Analysis Is Needed? /Computing Software Packages / Plan of the Book / COUNTERFACTUAL FRAMEWORK ANDASSUMPTIONS / Causality, Internal Validity, and Threats / Counterfactuals andthe Neyman-Rubin Counterfactual Framework / The Ignorable Treatment Assignment Assumption / The Stable Unit Treatment Value Assumption / Methods to Estimate Treatment Effects / Types of Treatment Effects / Heckman's Econometric Modelof Causality / Conclusions / SIMPLE METHODS FOR DATA BALANCING / Why Is Data Balancing Necessary? A Heuristic Example / Three Methods of Data Balancing / Design of a Data Simulation / Results of the Data Simulation / Implications of the Data Simulation / Key Issues regarding the Applications of OLS Regression / Conclusions / SAMPLE SELECTION AND RELATED MODELS / Heckman's Sample Selection Model / Treatment Effect Model / Instrumental Variables Estimator / Overview of the Stata Programs and Main Features treatreg / Illustrating Examples / Conclusions / PROPENSITY SCORE MATCHING AND RELATED MODELS / Overview / The Problem of Dimensionality and Properties of Propensity Scores / Estimating Propensity Scores / Matching / Post-Matching Analysis / Propensity Score Weighting Analysis / Modeling Doses of Treatment / Overview of the Stata and R Programs /Illustrating Examples / Conclusions / MATCHING ESTIMATORS / Overview / Methods of Matching Estimators / Overview of the Stata Program nnmatch / Illustrating Examples / Conclusions / PROPENSITY SCORE ANALYSIS WITH NONPARAMETRIC REGRESSION / Overview / Methods of Propensity Score Analysis with Nonparametric Regression / Overview of the Stata Programs psmatch2 and bootstrap / Illustrating Examples / Conclusions / 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 / IllustratingExamples / Conclusions / CONCLUDING REMARKS / Common Pitfalls in Observational Studies: A Checklist for Critical Review / Criticisms and Debates about Approximating Experiments with Propensity Score Approaches / Other Advances in Modeling Causality / Directions for Future Development / References

• ISBN: 978-1-4129-5356-6
• Editorial: Sage Publications
• Encuadernacion: Cartoné
• Páginas: 384
• Fecha Publicación: 01/09/2009
• Nº Volúmenes: 1
• Idioma: Inglés