Comparer des méthodes

	Effet Traitement Moyen Local (ETML / CACE)	Effets Hétérogènes du Traitement (CATE / Méta-Apprenants)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1994	2018
Auteur d'origine	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)	Wager & Athey (causal forest); Künzel et al. (meta-learners)
Type	Instrumental-variable causal estimand	Causal machine-learning framework
Source fondatrice	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.
Alias	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)	conditional average treatment effect, CATE, meta-learners, causal forest
Apparentées	5	5
Résumé	The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.	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).