Uporedite metode

	Heterogeni efekti tretmana (CATE / Meta-učenici)	Slučajna šuma
Oblast	Kauzalno zaključivanje	Mašinsko učenje
Porodica	Regression model	Machine learning
Godina nastanka	2018	2001
Tvorac	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Breiman, L.
Tip	Causal machine-learning framework	Ensemble (bagging of decision trees)
Temeljni izvor	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Drugi nazivi	conditional average treatment effect, CATE, meta-learners, causal forest	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Srodne	5	4
Sažetak	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).	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.