Comparar métodos

	Método de Controle Sintético para Avaliação de Políticas	Diferenças em Diferenças (DiD)
Área	Inferência causal	Econometria
Família	Regression model	Regression model
Ano de origem	2003-2010	1994
Autor original	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Causal inference / comparative case study	Causal inference / panel regression
Fonte seminal	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Outros nomes	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	5	5
Resumo	The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.