Vertaile menetelmiä

	Kaksoiskoneoppiminen	Random Forest
Tieteenala	Kausaalipäättely	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2018	2001
Kehittäjä	Victor Chernozhukov et al.	Breiman, L.
Tyyppi	Semiparametric causal estimation	Ensemble (bagging of decision trees)
Alkuperäislähde	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Rinnakkaisnimet	Debiased Machine Learning, Neyman Orthogonal Score Estimation, Partialing-Out Lasso, Çift Makine Öğrenmesi	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liittyvät	3	4
Tiivistelmä	Double/Debiased Machine Learning (DML), introduced by Chernozhukov et al. (2018), is a semiparametric framework for estimating causal or structural parameters in the presence of high-dimensional controls. It uses flexible machine learning methods to model nuisance functions—the conditional expectations of the outcome and the treatment given covariates—and then constructs a debiased estimator of the target parameter that achieves root-n consistency and valid inference despite the regularization bias inherent in high-dimensional settings.	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.