Comparar métodos

	Modelo Estructural Marginal (MSM) Aumentado con Aprendizaje Automático (ML-MSM)	Modelo Estructural Marginal (MSM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000 (MSM); 2011 (ML-augmented via targeted learning)	2000
Autor original	Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Causal inference / semiparametric weighted regression	Causal model / semiparametric weighting
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., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	ML-MSM, ML-augmented MSM, data-adaptive MSM, TMLE-MSM	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.