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空间双重稳健估计×双重差分法 (Diff-in-Diff)×
领域因果推断计量经济学
方法族Regression modelRegression model
起源年份2010s–2020s1994
提出者Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literatureCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
类型Semiparametric causal estimatorCausal inference / panel regression
开创性文献Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
别名Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimationdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
相关55
摘要Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.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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ScholarGate方法对比: Spatial Doubly Robust Estimation · Difference-in-Differences. 于 2026-06-15 检索自 https://scholargate.app/zh/compare