Módszerek összehasonlítása

	Robusztus Propensity Score Weighting	Marginal Structural Model (MSM)
Tudományterület	Oksági következtetés	Oksági következtetés
Módszercsalád	Regression model	Regression model
Keletkezés éve	1994–2019	2000
Megalkotó	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	James M. Robins, Miguel A. Hernan, Babette Brumback
Típus	Robust causal weighting estimator	Causal model / semiparametric weighting
Alapmű	Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alternatív nevek	robust PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Kapcsolódó	6	5
Összefoglaló	Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.