Сравнение методов

	Динамический анализ прерванных временных рядов	Динамический метод разностей-разностей
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2002–2017	2021
Автор метода	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini	Callaway & Sant'Anna; Sun & Abraham
Тип	Quasi-experimental time-series design	Causal inference / quasi-experimental
Основополагающий источник	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.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Другие названия	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Связанные	4	4
Сводка	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.	Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.