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	Bayesiansk matchning med propensity score	Propensity score-matchning
Ämnesområde	Kausal inferens	Forskningsstatistik
Familj	Regression model	Process / pipeline
Ursprungsår	2012	1983
Upphovsperson	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Paul Rosenbaum and Donald Rubin
Typ	Bayesian causal inference / matching	Method
Ursprungskälla	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
Närliggande	6	3
Sammanfattning	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.