Comparer des méthodes

	Appariement par score de propension pour l'effet de traitement hétérogène	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1983–2016	1983
Auteur d'origine	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Paul Rosenbaum and Donald Rubin
Type	Causal inference / matching with effect heterogeneity	Method
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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Alias	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	PSM, propensity score weighting, covariate balance
Apparentées	5	3
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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.