Сравнение методов

	Взвешивание по обратной вероятности для панельных данных	Маргинальная структурная модель (MSM)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000	2000
Автор метода	Robins, Hernan & Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Тип	Reweighting / causal inference	Causal model / semiparametric weighting
Основополагающий источник	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.
Другие названия	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Связанные	5	5
Сводка	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.	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.