Salīdzināt metodes

	Marginalā strukturālā modelēšana politikas novērtēšanai	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2000	2000
Autors	James M. Robins, Miguel A. Hernan, Babette Brumback	Robins, Hernán & Brumback
Tips	Causal inference / weighted regression	Causal inference 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Saistītās	6	5
Kopsavilkums	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.