เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การประเมินนโยบายแบบอนุกรมเวลาขัดจังหวะ× | Difference-in-Differences (DiD)× | |
|---|---|---|
| สาขาวิชา≠ | การอนุมานเชิงสาเหตุ | เศรษฐมิติ |
| ตระกูล | Regression model | Regression model |
| ปีกำเนิด≠ | 1975 (intervention analysis); 2000s–2010s (policy evaluation framing) | 1994 |
| ผู้ริเริ่ม≠ | Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| ประเภท≠ | Quasi-experimental causal design | Causal inference / panel regression |
| แหล่งต้นตำรับ≠ | 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 ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| ชื่อเรียกอื่น≠ | ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| ที่เกี่ยวข้อง≠ | 4 | 5 |
| สรุป≠ | 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. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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