方法对比

	异质性处理效应（CATE / 元学习器）	前门调整（前门准则）
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2018	1995
提出者	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Judea Pearl
类型	Causal machine-learning framework	Causal identification (graphical adjustment)
开创性文献	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.
别名	conditional average treatment effect, CATE, meta-learners, causal forest	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
相关	5	4
摘要	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).	Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.