Comparar métodos

	Método de Control Sintético Espacial	Series de Tiempo Interrumpidas Espaciales
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2003–2010s	1990s–2000s
Autor original	Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work	Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s
Tipo	Quasi-experimental causal inference	Quasi-experimental causal inference with spatial adjustment
Fuente seminal	Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.	McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications. ISBN: 978-0803913950
Alias	spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control	Spatial ITS, Geospatial ITS, Spatially-adjusted ITS, SITS
Relacionados	6	6
Resumen	The Spatial Synthetic Control Method adapts the classic synthetic control framework to settings where treated and donor units are defined by geographic location. By constructing a weighted combination of spatially proximate or comparable control regions, the method estimates what would have happened to a treated area absent the intervention, while explicitly accounting for geographic spillovers, spatial autocorrelation, and contiguity among units.	Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers.