Сравнение методов

	Multi-period Inverse Probability Weighting	Взвешивание по обратной вероятности лечения (IPW / IPTW)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2000	2000
Автор метода	Robins, Hernan & Brumback	Robins, Hernán & Brumback
Тип	Weighted causal estimator	Causal inference 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., Hernán, 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	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Связанные	6	5
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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.