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	Doubly Robust Estimation in Education Research	अंतर-में-अंतर (डिफ-इन-डिफ)
क्षेत्र	कारणात्मक अनुमान	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1994-2005	1994
प्रवर्तक	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
प्रकार	Causal inference / semiparametric estimator	Causal inference / panel regression
मौलिक स्रोत	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
उपनाम	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)
संबंधित	6	5
सारांश	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.