Policy Evaluation Interrupted Time Series
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.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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: 10.1093/ije/dyw098 ↗
- Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79. DOI: 10.1080/01621459.1975.10480264 ↗
Slik siterer du denne siden
ScholarGate. (2026, June 3). Interrupted Time Series Analysis for Policy Evaluation. ScholarGate. https://scholargate.app/no/causal-inference/policy-evaluation-interrupted-time-series
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Differanse-i-differanser (DiD)Økonometri↔ compare
- Tidsrekkeanalyse med avbrudd (Interrupted Time Series, ITS)Kausal inferens↔ compare
- Policy Evaluation Difference-in-DifferencesKausal inferens↔ compare
- Syntetisk kontrollmetode (SCM)Kausal inferens↔ compare
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