Salīdzināt metodes

	Daudzperiodu apgrieztā varbūtības svēršana	Dinamiskā apgrieztā varbūtības svēršana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000	1986-2000
Autors	Robins, Hernan & Brumback	James M. Robins and colleagues
Tips	Weighted causal estimator	Causal weighting estimator
Pirmavots	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.
Citi nosaukumi	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW
Saistītās	6	4
Kopsavilkums	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.