Salīdzināt metodes

	Heterogēni ārstēšanas efekti (CATE / Metamācītāji)	Frontdoor Adjustment (Frontdoor Criterion)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2018	1995
Autors	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Judea Pearl
Tips	Causal machine-learning framework	Causal identification (graphical adjustment)
Pirmavots	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.
Citi nosaukumi	conditional average treatment effect, CATE, meta-learners, causal forest	frontdoor criterion, Pearl's frontdoor adjustment, frontdoor formula, Ön Kapı Düzenlemesi (Frontdoor Adjustment)
Saistītās	5	4
Kopsavilkums	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.