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	बहु-अवधि व्युत्क्रम संभाव्यता भारण (Multi-period Inverse Probability Weighting)	गतिशील व्युत्क्रम प्रायिकता भारण
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2000	1986-2000
प्रवर्तक	Robins, Hernan & Brumback	James M. Robins and colleagues
प्रकार	Weighted causal estimator	Causal weighting estimator
मौलिक स्रोत	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.
उपनाम	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW
संबंधित	6	4
सारांश	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.