Salīdzināt metodes

	Dubultā mašīnmācīšanās	Heterogēni ārstēšanas efekti (CATE / Metamācītāji)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Machine learning	Regression model
Izcelsmes gads	2018	2018
Autors	Victor Chernozhukov et al.	Wager & Athey (causal forest); Künzel et al. (meta-learners)
Tips	Semiparametric causal estimation	Causal machine-learning framework
Pirmavots	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.	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.
Citi nosaukumi	Debiased Machine Learning, Neyman Orthogonal Score Estimation, Partialing-Out Lasso, Çift Makine Öğrenmesi	conditional average treatment effect, CATE, meta-learners, causal forest
Saistītās	3	5
Kopsavilkums	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.	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).