Comparar métodos

	Método de Control Sintético Espacial	Diseño de Discontinuidad por Regresión Espacial (Spatial RDD)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2003–2010s	2010s
Autor original	Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Tipo	Quasi-experimental causal inference	Quasi-experimental causal inference
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.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Relacionados	6	4
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 Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.