Comparer des méthodes

	Estimateur d'appariement des effets de traitement hétérogènes	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1997-2006	1973
Auteur d'origine	Heckman, Ichimura & Todd; Abadie & Imbens	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Causal inference / nonparametric matching	Nonparametric 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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	nearest-neighbor matching, NNM, matching on covariates, covariate 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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.