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	विषम उपचार प्रभाव कोर्स्ड एग्जैक्ट मैचिंग	कोर्स्ड एग्जैक्ट मैचिंग (CEM)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2012-2013	2011-2012
प्रवर्तक	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Iacus, King, & Porro
प्रकार	Matching-based causal inference with subgroup CATE estimation	Matching / causal inference
मौलिक स्रोत	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.
उपनाम	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	CEM, coarsened matching, monotonic imbalance bounding matching
संबंधित	5	6
सारांश	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.