Comparer des méthodes

	Effets Hétérogènes du Traitement (CATE / Méta-Apprenants)	Variables instrumentales par moindres carrés en deux étapes (VI/2SLS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2018	2009
Auteur d'origine	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Type	Causal machine-learning framework	Instrumental-variables regression
Source fondatrice	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	conditional average treatment effect, CATE, meta-learners, causal forest	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Apparentées	5	5
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).	IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).