Comparar métodos

	Modelo Estructural Marginal (MSM) Aumentado con Aprendizaje Automático (ML-MSM)	Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)
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)	Robins, Hernán & Brumback
Tipo	Causal inference / semiparametric weighted regression	Causal inference weighting 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., Hernán, 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	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
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.	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.