Linganisha mbinu

	Uzito wa Kujumuisha Uwezekano wa Kinyume kwa Vipindi Nyingi	Uzito wa Uwezekano Geu Kinabadilika
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000	1986-2000
Mwanzilishi	Robins, Hernan & Brumback	James M. Robins and colleagues
Aina	Weighted causal estimator	Causal weighting estimator
Chanzo asilia	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.
Majina mbadala	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW
Zinazohusiana	6	4
Muhtasari	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.