方法对比

	空间工具变量（Spatial IV / Spatial 2SLS）	空间双重稳健估计
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1988-1998	2010s–2020s
提出者	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature
类型	Quasi-experimental causal inference with spatial dependence	Semiparametric causal estimator
开创性文献	Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17(1), 99-121.	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.
别名	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
相关	6	5
摘要	Spatial Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighboring units, then applies two-stage least squares to recover unbiased causal estimates in the presence of both endogenous regressors and spatial autocorrelation.	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.