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	Bayesowskie balansowanie entropii	Bayesian Propensity Score Matching
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2012-2020s	2012
Twórca	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Typ	Weighting-based causal estimator with Bayesian uncertainty quantification	Bayesian causal inference / matching
Źródło pierwotne	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.	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	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Pokrewne	6	6
Podsumowanie	Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.	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.