Módszerek összehasonlítása

	Marginal Structural Model a Politikai Értékeléshez	Propensity Score Weighting (PSW / IPW)
Tudományterület	Oksági következtetés	Oksági következtetés
Módszercsalád	Regression model	Regression model
Keletkezés éve	2000	1983 (propensity score); 2003 (efficient IPW estimator)
Megalkotó	James M. Robins, Miguel A. Hernan, Babette Brumback	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Típus	Causal inference / weighted regression	Causal inference / reweighting
Alapmű	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Alternatív nevek	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	PSW, inverse probability weighting, IPW, propensity-based weighting
Kapcsolódó	6	6
Összefoglaló	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.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).