Vertaile menetelmiä

	Kohdennettu suurimman uskottavuuden estimointi (TMLE)	Kaksoisrobustin estimoinnin (AIPW) menetelmä
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Machine learning	Regression model
Syntyvuosi	2006	2005
Kehittäjä	Mark van der Laan & Daniel Rubin	Robins & Rotnitzky; Bang & Robins
Tyyppi	Semiparametric estimator	Semiparametric causal estimator
Alkuperäislähde	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).	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.
Rinnakkaisnimet	Targeted Learning, TMLE, Targeted MLE, Hedeflenmiş Maksimum Olabilirlik Tahmini	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Liittyvät	3	5
Tiivistelmä	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.	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.