Сравнение на методи

	Проверка с пространствено плацебо	Spatial Instrumental Variables
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2000s–2010s	1988-1998
Създател	Developed organically in spatial econometrics and geographic RDD literature; prominent use in Dell (2010) and related work	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)
Тип	Falsification / robustness check	Quasi-experimental causal inference with spatial dependence
Основополагащ източник	Buonanno, P., Montolio, D., & Vanin, P. (2009). Does Social Capital Reduce Crime? Journal of Law and Economics, 52(1), 145-170.	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.
Други названия	geographic placebo test, spatial falsification test, spatial robustness check, geographic spillover test	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV
Свързани	4	6
Резюме	A spatial placebo test is a falsification check used in geographic or spatial causal-inference studies. The analyst applies the same estimation procedure to spatial units, boundaries, or zones where no treatment effect should exist — fake borders, shifted cutoffs, or buffer areas beyond spillover range — and checks whether a spurious effect emerges. A non-significant result in the placebo region supports the credibility of the main causal estimate.	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.