Porównaj metody

	Bayesian Propensity Score Matching	Coarsened Exact Matching (CEM)
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2012	2011-2012
Twórca	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Iacus, King, & Porro
Typ	Bayesian causal inference / matching	Matching / causal inference
Źródło pierwotne	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Inne nazwy	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	CEM, coarsened matching, monotonic imbalance bounding matching
Pokrewne	6	6
Podsumowanie	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.	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.