方法对比
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| 政策评估中断时间序列× | 政策评估双重差分法× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1975 (intervention analysis); 2000s–2010s (policy evaluation framing) | 1978-2009 |
| 提出者≠ | 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) |
| 类型≠ | Quasi-experimental causal design | Quasi-experimental / policy evaluation |
| 开创性文献≠ | 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 ↗ | Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. DOI ↗ |
| 别名 | ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS | policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment |
| 相关 | 4 | 4 |
| 摘要≠ | 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. |
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