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	Spatial Synthetic Control Method	Spatial Regression Discontinuity Design (Spatial RDD)
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2003–2010s	2010s
Upphovsperson	Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Typ	Quasi-experimental causal inference	Quasi-experimental causal inference
Ursprungskälla	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
Närliggande	6	4
Sammanfattning	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.