Σύγκριση μεθόδων

	Ετερογενείς Επιδράσεις Θεραπείας (CATE / Μετα-Μαθητές)	Εκτιμητές Μεταβλητών-Εργαλείων μέσω Ελαχίστων Τετραγώνων Δύο Σταδίων (IV/2SLS)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2018	2009
Δημιουργός	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Τύπος	Causal machine-learning framework	Instrumental-variables regression
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	conditional average treatment effect, CATE, meta-learners, causal forest	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Συναφείς	5	5
Σύνοψη	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).