Võrdle meetodeid

	Heterogeneous Treatment Effects	Instrumentaalmuutujad kaheastmelise vähimruutude meetodi abil (IV/2SLS)
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2018	2009
Looja	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)
Tüüp	Causal machine-learning framework	Instrumental-variables regression
Algallikas	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
Rööpnimetused	conditional average treatment effect, CATE, meta-learners, causal forest	instrumental variables, IV estimation, 2SLS, instrumental variable regression
Seotud	5	5
Kokkuvõte	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).