方法对比

	贝叶斯粗化精确匹配	粗化精确匹配 (CEM)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2011-2012	2011-2012
提出者	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Iacus, King, & Porro
类型	Quasi-experimental matching with Bayesian inference	Matching / causal inference
开创性文献	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
别名	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	CEM, coarsened matching, monotonic imbalance bounding matching
相关	6	6
摘要	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.	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.