手法を比較

	教育研究における二重頑健推定量	傾向スコア重み付け（PSW / IPW）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	1994-2005	1983 (propensity score); 2003 (efficient IPW estimator)
提唱者	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
種類	Causal inference / semiparametric estimator	Causal inference / reweighting
原典	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.
別名	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
関連	6	6
概要	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).