Vertaile menetelmiä

	Kaksoisrobustin estimoinnin käyttö koulutustutkimuksessa	Käänteisen todennäköisyyden painotus (IPW / IPTW)
Tieteenala	Kausaalipäättely	Kausaalipäättely
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	1994-2005	2000
Kehittäjä	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Robins, Hernán & Brumback
Tyyppi	Causal inference / semiparametric estimator	Causal inference weighting estimator
Alkuperäislähde	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Rinnakkaisnimet	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Liittyvät	6	5
Tiivistelmä	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.	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.