Сравнение на методи

	Spatial Instrumental Variables	Пространствена двойно робастна оценка
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	1988-1998	2010s–2020s
Създател	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
Тип	Quasi-experimental causal inference with spatial dependence	Semiparametric causal estimator
Основополагащ източник	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.
Други названия	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
Свързани	6	5
Резюме	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.