Uporedite metode

	Lokalni prosečan efekat tretmana (LATE / CACE)	Heterogeni efekti tretmana (CATE / Meta-učenici)
Oblast	Kauzalno zaključivanje	Kauzalno zaključivanje
Porodica	Regression model	Regression model
Godina nastanka	1994	2018
Tvorac	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)	Wager & Athey (causal forest); Künzel et al. (meta-learners)
Tip	Instrumental-variable causal estimand	Causal machine-learning framework
Temeljni izvor	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.
Drugi nazivi	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)	conditional average treatment effect, CATE, meta-learners, causal forest
Srodne	5	5
Sažetak	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).