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	Estymator bayesowskiego dopasowania	Bayesian Propensity Score Matching
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	1978–1998	2012
Twórca	Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Typ	Bayesian causal inference / nonparametric matching	Bayesian causal inference / matching
Źródło pierwotne	Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.
Inne nazwy	Bayesian matching, Bayesian nonparametric matching, Bayes-ATE matching, posterior matching estimator	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Pokrewne	6	6
Podsumowanie	The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distribution rather than relying on asymptotic standard errors, yielding credible intervals that reflect both sampling variability and prior knowledge.	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.