Сравнение на методи

	Двойно машинно обучение	Двойно устойчива оценка (AIPW)	Хетерогенни ефекти от лечение (CATE / Мета-обучаващи алгоритми)
Област	Причинно-следствено заключение	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Machine learning	Regression model	Regression model
Година на възникване	2018	2005	2018
Създател	Victor Chernozhukov et al.	Robins & Rotnitzky; Bang & Robins	Wager & Athey (causal forest); Künzel et al. (meta-learners)
Тип	Semiparametric causal estimation	Semiparametric causal estimator	Causal machine-learning framework
Основополагащ източник	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.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.
Други названия	Debiased Machine Learning, Neyman Orthogonal Score Estimation, Partialing-Out Lasso, Çift Makine Öğrenmesi	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)	conditional average treatment effect, CATE, meta-learners, causal forest
Свързани	3	5	5
Резюме	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.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.	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).