Comparer des méthodes

	Estimateur d'appariement des effets de traitement hétérogènes	Coarsened Exact Matching (CEM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1997-2006	2011-2012
Auteur d'origine	Heckman, Ichimura & Todd; Abadie & Imbens	Iacus, King, & Porro
Type	Causal inference / nonparametric matching	Matching / causal inference
Source fondatrice	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic Studies, 64(4), 605-654.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	CEM, coarsened matching, monotonic imbalance bounding matching
Apparentées	6	6
Résumé	The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all.	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.