Regression modelQuasi-experimental / causal inference

Heterogeneous Treatment Effect Interrupted Time Series (HTE-ITS)

Heterogeneous Treatment Effect Interrupted Time Series extends the standard ITS design to detect whether an intervention's effect on a time series differs systematically across subgroups or in response to unit-level moderators. Where ordinary ITS yields a single level-change and slope-change estimate, HTE-ITS adds interaction terms for a moderating variable, revealing who benefits more or less from the intervention and by how much.

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Sources

  1. Lopez Bernal, J., 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. Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750. DOI: 10.1136/bmj.h2750

Related methods

ScholarGateHeterogeneous Treatment Effect Interrupted Time Series (Heterogeneous Treatment Effect Interrupted Time Series Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-interrupted-time-series