Comparer des méthodes

	Appariement par score de propension pour l'effet de traitement hétérogène	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983–2016	1973
Auteur d'origine	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Causal inference / matching with effect heterogeneity	Nonparametric matching / causal inference
Source fondatrice	Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	5	6
Résumé	Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.	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.