方法对比

	贝叶斯熵平衡	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	2012-2020s	2011-2012
提出者	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Iacus, King, & Porro
类型	Weighting-based causal estimator with Bayesian uncertainty quantification	Matching / causal inference
开创性文献	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
别名	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	CEM, coarsened matching, monotonic imbalance bounding matching
相关	6	6
摘要	Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.	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.