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| السلاسل الزمنية المتقطعة الديناميكية× | Dynamic Difference-in-Differences× | |
|---|---|---|
| المجال | الاستدلال السببي | الاستدلال السببي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 2002–2017 | 2021 |
| صاحب الطريقة≠ | Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini | Callaway & Sant'Anna; Sun & Abraham |
| النوع≠ | Quasi-experimental time-series design | Causal inference / quasi-experimental |
| المصدر التأسيسي≠ | 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. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| الأسماء البديلة | Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| ذات صلة | 4 | 4 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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