Comparar métodos

	Series de Tiempo Interrumpidas para Evaluación de Políticas	Método de Control Sintético (SCM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)	2003–2010
Autor original	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Quasi-experimental causal design	Quasi-experimental causal inference
Fuente seminal	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.	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	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	4	4
Resumen	Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.	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.