Confronta i metodi

	Metodo del Controllo Sintetico Spaziale	Disegno di Regressione con Discontinuità Spaziale (Spatial RDD)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2003–2010s	2010s
Ideatore	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
Fonte seminale	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
Correlati	6	4
Sintesi	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.