Comparar métodos

	Series de Tiempo Interrumpidas para Evaluación de Políticas	Evaluación de Políticas Diferencias en Diferencias
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)	1978-2009
Autor original	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)
Tipo	Quasi-experimental causal design	Quasi-experimental / policy evaluation
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.	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.
Alias	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment
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.	Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.