Uporedite metode

	Dinamijsko ponderisanje inverznim verovatnoćama	Metoda inverzne verovatnoće tretmana (IPW / IPTW)
Oblast	Kauzalno zaključivanje	Kauzalno zaključivanje
Porodica	Regression model	Regression model
Godina nastanka	1986-2000	2000
Tvorac	James M. Robins and colleagues	Robins, Hernán & Brumback
Tip	Causal weighting estimator	Causal inference weighting estimator
Temeljni izvor	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.
Drugi nazivi	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Srodne	4	5
Sažetak	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.