Comparer des méthodes

	Estimation ciblée du maximum de vraisemblance (TMLE)	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Machine learning	Regression model
Année d'origine	2006	2000
Auteur d'origine	Mark van der Laan & Daniel Rubin	Robins, Hernán & Brumback
Type	Semiparametric estimator	Causal inference weighting estimator
Source fondatrice	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	Targeted Learning, TMLE, Targeted MLE, Hedeflenmiş Maksimum Olabilirlik Tahmini	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	3	5
Résumé	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.