विधियों की तुलना करें

	विषम उपचार प्रभाव (CATE / मेटा-लर्नर्स)	फ्रंटडोर एडजस्टमेंट (फ्रंटडोर क्राइटेरियन)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2018	1995
प्रवर्तक	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Judea Pearl
प्रकार	Causal machine-learning framework	Causal identification (graphical adjustment)
मौलिक स्रोत	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.
उपनाम	conditional average treatment effect, CATE, meta-learners, causal forest	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
संबंधित	5	4
सारांश	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.