Compară metode

	Model Robust cu Structură Marginală	Model Structural Marginal (MSM) pentru Date Panel
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2000–2004	2000
Autorul original	Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian	James M. Robins, Miguel A. Hernan, Babette Brumback
Tip	Causal inference / weighted regression	Causal model for time-varying treatments
Sursa seminală	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.
Denumiri alternative	robust MSM, doubly-robust MSM, sandwich-SE MSM, robust IPTW marginal structural model	MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM
Înrudite	6	5
Rezumat	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.