방법 비교

	이질적 처리 효과 조밀화된 정확 매칭 (Heterogeneous Treatment Effect Coarsened Exact Matching)	Coarsened Exact Matching (CEM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2012-2013	2011-2012
창시자	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Iacus, King, & Porro
유형	Matching-based causal inference with subgroup CATE estimation	Matching / causal inference
원전	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.
별칭	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	CEM, coarsened matching, monotonic imbalance bounding matching
관련	5	6
요약	Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much.	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.