Comparar métodos

	Spatial Instrumental Variables	Análisis de Impacto Causal Espacial
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1988-1998	2010s (codified)
Autor original	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)	Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)
Tipo	Quasi-experimental causal inference with spatial dependence	Quasi-experimental causal inference with spatial data
Fuente seminal	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.	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126.
Alias	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation
Relacionados	6	4
Resumen	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 causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas.