Comparer des méthodes

	Effets Hétérogènes du Traitement (CATE / Méta-Apprenants)	Ajustement par la porte de devant (Critère de la porte de devant)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2018	1995
Auteur d'origine	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Judea Pearl
Type	Causal machine-learning framework	Causal identification (graphical adjustment)
Source fondatrice	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.
Alias	conditional average treatment effect, CATE, meta-learners, causal forest	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
Apparentées	5	4
Résumé	Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).	Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.