Comparar métodos

	Modelo Estructural Marginal Robusto	Modelo Estructural Marginal (MSM) de Datos de Panel
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000–2004	2000
Autor original	Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Causal inference / weighted regression	Causal model for time-varying treatments
Fuente seminal	Robins, J. M., Hernán, 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	robust MSM, doubly-robust MSM, sandwich-SE MSM, robust IPTW marginal structural model	MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM
Relacionados	6	5
Resumen	Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors or doubly-robust estimators. This combination yields valid causal estimates and reliable inference even when the outcome regression model is mildly misspecified or weights are moderately variable.	A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle.