Comparar métodos

	Método de Control Sintético Dinámico	Método de Control Sintético (SCM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010	2003–2010
Autor original	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Comparative case study / counterfactual estimation	Quasi-experimental causal inference
Fuente 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.	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	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	5	4
Resumen	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.	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.