Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations of PSA and illustrates how it can be used for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems related to, strategies for employing, and the limitations of PSA. Unlike the existing textbooks on program evaluation, Propensity Score Analysis delves into statistical concepts, formulas, and models underlying the application.
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Key Features

• Presents key information on model derivationsA?
• Summarizes complex statistical arguments but omits their proofs
• Links each method found in this book to specific Stata programs and provides empirical examplesA?
• Guides readers using two conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman econometric model of causalityA?
• Contains examples representing real challenges commonly found in social behavioral researchA?
• Utilizes data simulation and Monte Carlo studies to illustrate key pointsA?
• Presents descriptions of new statistical approaches necessary for understanding the four evaluation methods incorporated throughout the text

Intended Audience
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This text is appropriate for graduate and doctoral students taking Evaluation, Quantitative Methods, Survey Research, and Research Design courses across business, social work, public policy, psychology, sociology, and health/medicine disciplines.

Counterfactual framework and assumptions -- Conventional methods for data balancing -- Sample selection and related models -- Propensity score matching and related models -- Matching estimators -- Propensity score analysis with nonparametric regression -- Selection bias and sensitivity analysis -- Concluding remarks.