Сравнение методов

	Робастная маргинальная структурная модель	Панельная маргинальная структурная модель (MSM)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000–2004	2000
Автор метода	Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian	James M. Robins, Miguel A. Hernan, Babette Brumback
Тип	Causal inference / weighted regression	Causal model for time-varying treatments
Основополагающий источник	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.
Другие названия	robust MSM, doubly-robust MSM, sandwich-SE MSM, robust IPTW marginal structural model	MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM
Связанные	6	5
Сводка	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.