手法を比較

	局所的平均処置効果（LATE / CACE）	異質的処置効果（CATE / メタ学習器）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	1994	2018
提唱者	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)	Wager & Athey (causal forest); Künzel et al. (meta-learners)
種類	Instrumental-variable causal estimand	Causal machine-learning framework
原典	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.
別名	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)	conditional average treatment effect, CATE, meta-learners, causal forest
関連	5	5
概要	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).