Salīdzināt metodes

	Heterogēni ārstēšanas efekti (CATE / Metamācītāji)	Random Forest
Nozare	Cēloņsakarību secināšana	Mašīnmācīšanās
Saime	Regression model	Machine learning
Izcelsmes gads	2018	2001
Autors	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Breiman, L.
Tips	Causal machine-learning framework	Ensemble (bagging of decision trees)
Pirmavots	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.
Citi nosaukumi	conditional average treatment effect, CATE, meta-learners, causal forest	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
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).	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.