পদ্ধতির তুলনা করুন

	বিষমধর্মী চিকিৎসা প্রভাব (CATE / মেটা-লার্নার)	Random Forest
ক্ষেত্র	কার্যকারণ অনুমান	যন্ত্র শিখন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2018	2001
প্রবর্তক	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Breiman, L.
ধরন	Causal machine-learning framework	Ensemble (bagging of decision trees)
মৌলিক উৎস	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.
অপর নাম	conditional average treatment effect, CATE, meta-learners, causal forest	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
সম্পর্কিত	5	4
সারসংক্ষেপ	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.