Jämför metoder

	Bayesiansk grovsorterad exakt matchning	Bayesiansk matchning med propensity score
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2011-2012	2012
Upphovsperson	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)
Typ	Quasi-experimental matching with Bayesian inference	Bayesian causal inference / matching
Ursprungskälla	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
Närliggande	6	6
Sammanfattning	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.