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	Marginalny Model Strukturalny Oceny Polityki	Ważenie odwrotnością prawdopodobieństwa leczenia (IPW / IPTW)
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2000	2000
Twórca	James M. Robins, Miguel A. Hernan, Babette Brumback	Robins, Hernán & Brumback
Typ	Causal inference / weighted regression	Causal inference weighting estimator
Źródło pierwotne	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.
Inne nazwy	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Pokrewne	6	5
Podsumowanie	A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.	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.