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	Dynamic Inverse Probability Weighting	Viktning med inversa sannolikheter för behandling (IPW / IPTW)
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	1986-2000	2000
Upphovsperson	James M. Robins and colleagues	Robins, Hernán & Brumback
Typ	Causal weighting estimator	Causal inference weighting estimator
Ursprungskälla	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.
Alias	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Närliggande	4	5
Sammanfattning	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.	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.