Comparer des méthodes

	Différence-en-différences spatiale	Méthode du Contrôle Synthétique (SCM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015	2010
Auteur d'origine	Delgado & Florax	Abadie, Diamond & Hainmueller
Type	Quasi-experimental estimator	Counterfactual causal-inference model
Source fondatrice	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Alias	Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
Apparentées	3	5
Résumé	Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms.	The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.