Comparar métodos

	Análise de Impacto Causal Espacial	Método do Controle Sintético (MCS)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	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)
Tipo	Quasi-experimental causal inference with spatial data	Quasi-experimental causal inference
Fonte 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.
Outros nomes	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	4	4
Resumo	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.