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रोबस्ट मैचिंग एस्टिमेटर (बायस-करेक्टेड मैचिंग)×अंतर-में-अंतर (डिफ-इन-डिफ)×
क्षेत्रकारणात्मक अनुमानअर्थमिति
परिवारRegression modelRegression model
उद्भव वर्ष2006/20111994
प्रवर्तकAbadie & ImbensCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
प्रकारCausal inference / matchingCausal inference / panel regression
मौलिक स्रोतAbadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
उपनामbias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matchingdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
संबंधित65
सारांशThe robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.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.
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  3. PUBLISHED

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