Propensity Score Analysis: Statistical Methods and ApplicationsFully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application. |
Contents
INTRODUCTION | 1 |
COUNTERFACTUAL FRAMEWORK AND ASSUMPTIONS | 21 |
CONVENTIONAL METHODS FOR DATA BALANCING | 67 |
SAMPLE SELECTION AND RELATED MODELS | 95 |
PROPENSITY SCORE MATCHING AND RELATED MODELS | 129 |
PROPENSITY SCORE SUBCLASSIFICATION | 203 |
PROPENSITY SCORE WEIGHTING | 239 |
MATCHING ESTIMATORS | 255 |
PROPENSITY SCORE ANALYSIS WITH NONPARAMETRIC REGRESSION | 283 |
PROPENSITY SCORE ANALYSIS OF CATEGORICAL OR CONTINUOUS TREATMENTS DOSAGE ANALYSES | 309 |
SELECTION BIAS AND SENSITIVITY ANALYSIS | 335 |
CONCLUDING REMARKS | 381 |
395 | |
409 | |
Other editions - View all
Propensity Score Analysis: Statistical Methods and Applications Shenyang Guo,Mark W. Fraser Limited preview - 2014 |
Propensity Score Analysis: Statistical Methods and Applications Shenyang Guo,Mark W. Fraser No preview available - 2009 |
Common terms and phrases
achievement adjustment AFDC analysis applied approach assume assumption average treatment effect balance calculation caregivers causal Chapter child clustering coefficients compared condition conduct continuous correct correlated covariates defined dependent described determine developed discussion distance distribution employed equation error evaluation example experiment Figure findings four framework function given Heckman ignorable illustrate Imbens important independent indicates inference intervention linear logistic regression matching estimators mean measured methods Note observational studies observed obtain optimal outcome pair participants perform population presents probability problem procedure produced propensity score randomized rank receiving reference regression researchers Rosenbaum Rubin running sample Scenario schools selection bias sensitivity similar social Source specification standard Stata statistical Step subclasses subclassification Table term tion treated treatment assignment types units users variable variance vector weighting