Comparar métodos

	Estimação de Máxima Verossimilhança Direcionada (TMLE)	Ponderação pela Probabilidade Inversa de Tratamento (IPW / IPTW)
Área	Inferência causal	Inferência causal
Família	Machine learning	Regression model
Ano de origem	2006	2000
Autor original	Mark van der Laan & Daniel Rubin	Robins, Hernán & Brumback
Tipo	Semiparametric estimator	Causal inference weighting estimator
Fonte seminal	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.
Outros nomes	Targeted Learning, TMLE, Targeted MLE, Hedeflenmiş Maksimum Olabilirlik Tahmini	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Relacionados	3	5
Resumo	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.