Compara mètodes

	Anàlisi d'Impacte Causal Espacial	Mètode del Control Sintètic (SCM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2010s (codified)	2003–2010
Autor original	Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipus	Quasi-experimental causal inference with spatial data	Quasi-experimental causal inference
Font seminal	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126.	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.
Àlies	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionats	4	4
Resum	Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.