방법 비교

	교육 연구에서의 이중으로 강건한 추정	성향 점수 가중치 (PSW / IPW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1994-2005	1983 (propensity score); 2003 (efficient IPW estimator)
창시자	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
유형	Causal inference / semiparametric estimator	Causal inference / reweighting
원전	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	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.
별칭	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	PSW, inverse probability weighting, IPW, propensity-based weighting
관련	6	6
요약	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.	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).