方法对比

	通过粗糙化精确匹配 (CEM) 进行政策评估	熵平衡
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2011-2012	2012
提出者	Iacus, King & Porro	Jens Hainmueller
类型	Matching / quasi-experimental design	Covariate-balancing reweighting
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.	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.
别名	CEM, Coarsened Exact Matching, CEM policy evaluation, coarsening-based matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
相关	5	6
摘要	Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.