Interrupted Time Series Analysis with Control

Including a control series in ITS analysis improves causal inference, as ITS cannot exclude the possibility that any observed change was due to the intervention of interest, or another co-intervention or event. A control series is one that is not impacted by the intervention; selection of an appropriate control is described elsewhere [3 (link)]. As with ITS in segmented regression, including a control series involves running an ARIMA model for the series of interest, and separately for the control series [17 (link)]. If a change is observed in the intervention series but not the control series, this provides evidence that the impact was specific to the intervention.