Сравнение методов

	Multi-period Inverse Probability Weighting	Динамическое взвешивание по обратной вероятности
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000	1986-2000
Автор метода	Robins, Hernan & Brumback	James M. Robins and colleagues
Тип	Weighted causal estimator	Causal weighting estimator
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	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW
Связанные	6	4
Сводка	Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.