Comparar métodos

	Coincidencia exacta tosca para efectos de tratamiento heterogéneos	Coarsened Exact Matching (CEM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012-2013	2011-2012
Autor original	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Iacus, King, & Porro
Tipo	Matching-based causal inference with subgroup CATE estimation	Matching / causal inference
Fuente seminal	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.
Alias	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	5	6
Resumen	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.