Bandingkan metode

	Pencocokan Tepat yang Dikasarankan Bayesian	Pencocokan Skor Propensitas Bayesian
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2011-2012	2012
Pencetus	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Tipe	Quasi-experimental matching with Bayesian inference	Bayesian causal inference / matching
Sumber perintis	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.
Alias	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting
Terkait	6	6
Ringkasan	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.	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.