Sammenlign metoder

	Dobbelt robust estimering inden for uddannelsesforskning	Marginal Structural Model (MSM)
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	1994-2005	2000
Ophavsperson	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Causal inference / semiparametric estimator	Causal model / semiparametric weighting
Oprindelig kilde	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Aliasser	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relaterede	6	5
Resumé	Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-random but observed covariates can account for selection bias. The estimator is consistent if either — not necessarily both — of the two component models is correctly 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.