방법 비교

	교육 연구에서의 이중으로 강건한 추정	이중차분법 (Diff-in-Diff)
분야	인과추론	계량경제학
계열	Regression model	Regression model
기원 연도	1994-2005	1994
창시자	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형	Causal inference / semiparametric estimator	Causal inference / panel regression
원전	Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
별칭	DR estimator in education, AIPW in education, augmented IPW in education research, doubly robust causal estimation for educational outcomes	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련	6	5
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.