手法を比較

	ベイズ型粗密厳密マッチング	傾向スコアマッチング
分野	因果推論	研究統計
系統	Regression model	Process / pipeline
提唱年	2011-2012	1983
提唱者	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Paul Rosenbaum and Donald Rubin
種類	Quasi-experimental matching with Bayesian inference	Method
原典	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
別名	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	PSM, propensity score weighting, covariate balance
関連	6	3
概要	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.