قارن الطرق

	التقدير المستهدف بأقصى احتمالية (TMLE)	التعلم الآلي المزدوج
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Machine learning	Machine learning
سنة النشأة	2006	2018
صاحب الطريقة	Mark van der Laan & Daniel Rubin	Victor Chernozhukov et al.
النوع	Semiparametric estimator	Semiparametric causal estimation
المصدر التأسيسي	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).	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.
الأسماء البديلة	Targeted Learning, TMLE, Targeted MLE, Hedeflenmiş Maksimum Olabilirlik Tahmini	Debiased Machine Learning, Neyman Orthogonal Score Estimation, Partialing-Out Lasso, Çift Makine Öğrenmesi
ذات صلة	3	3
الملخص	Targeted Maximum Likelihood Estimation (TMLE) is a semiparametric, doubly robust causal inference method introduced by Mark van der Laan and Daniel Rubin in 2006. It combines flexible machine learning models for both the outcome and the treatment assignment mechanism, then applies a targeting step that re-fits the initial outcome model specifically to reduce bias for a pre-specified causal estimand such as the average treatment effect. TMLE is widely used in epidemiology, biostatistics, and health economics when estimating causal effects from observational data.	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.