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	Bayesowskie dopasowanie dokładne zgrubne	Estymator dopasowujący
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2011-2012	1973
Twórca	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Typ	Quasi-experimental matching with Bayesian inference	Nonparametric matching / causal inference
Źródło pierwotne	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Inne nazwy	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Pokrewne	6	6
Podsumowanie	Bayesian Coarsened Exact Matching (Bayesian CEM) combines the coarsening-and-exact-matching framework of Iacus, King, and Porro with Bayesian posterior inference. Covariates are discretised into coarser bins so that treated and control units can be matched exactly within those bins, and Bayesian priors are then placed on the treatment-effect parameters to produce full posterior distributions over the causal estimand rather than a single point estimate.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.