Comparar métodos

	Modelo Estructural Marginal (MSM) Aumentado con Aprendizaje Automático (ML-MSM)	Estimación Doblemente Robusta (AIPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000 (MSM); 2011 (ML-augmented via targeted learning)	2005
Autor original	Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)	Robins & Rotnitzky; Bang & Robins
Tipo	Causal inference / semiparametric weighted regression	Semiparametric causal estimator
Fuente seminal	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	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.
Alias	ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	5	5
Resumen	The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nuisance models with ensemble learners or neural networks, ML-MSMs recover valid causal estimates under confounding without relying on correctly specified parametric forms.	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.