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| イベントスタディデザイン（因果イベントスタディ）× | 中断時系列分析（Interrupted Time Series, ITS）× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2021 | 2002 |
| 提唱者≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| 種類≠ | Dynamic causal panel regression | Quasi-experimental segmented regression |
| 原典≠ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ | 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. DOI ↗ |
| 別名≠ | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| 関連 | 5 | 5 |
| 概要≠ | The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021). | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
| ScholarGateデータセット ↗ |
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