Comparar métodos

	Series de Tiempo Interrumpidas Dinámicas	Diferencia en Diferencias (Diff-in-Diff)
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	2002–2017	1994
Autor original	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Quasi-experimental time-series design	Causal inference / panel regression
Fuente seminal	Lopez Bernal, J., 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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	4	5
Resumen	Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.	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.