Comparer des méthodes

	Appariement Bayésien par Score de Propension	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2012	1983
Auteur d'origine	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Paul Rosenbaum and Donald Rubin
Type	Bayesian causal inference / matching	Method
Source fondatrice	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	PSM, propensity score weighting, covariate balance
Apparentées	6	3
Résumé	Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.