Regression modelQuasi-experimental / causal inference

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.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. 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
  2. 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

Related methods

Referenced by

ScholarGatePolicy Evaluation Interrupted Time Series (Interrupted Time Series Analysis for Policy Evaluation). Retrieved 2026-06-04 from https://scholargate.app/tr/causal-inference/policy-evaluation-interrupted-time-series