🤖 AI Summary
This study addresses the bias in estimating intervention effects within single-unit interrupted time series analyses that arises from ignoring concurrent or preceding disruptive interruptions—such as the COVID-19 pandemic. To mitigate this issue, the paper introduces Bayesian stacking into this domain for the first time, constructing a robust counterfactual prediction model by integrating multiple functional forms representing potential confounding interruptions. By combining count data modeling with flexible functional ensembles, the proposed approach substantially enhances the reliability of counterfactual inference under co-occurring disruptions. The method is empirically applied to assess the impact of Texas’s six-week abortion ban in 2021 on reported pregnancy counts, effectively adjusting for pandemic-related confounding and accurately isolating the true policy effect.
📝 Abstract
Interrupted time series analysis has been used to model the effect of policy and other interventions on public health by forecasting a counterfactual time series during the intervention period using data from prior to the intervention. However, due to typically relying on a single study unit, this approach risks not adjusting for other interruptions that precede and co-occur during the intervention period. The COVID-19 pandemic is a prominent example of this phenomenon of nuisance interruptions in contemporary public health research. To address this complication, we propose using Bayesian stacking over a range of functional forms for the impact of the nuisance interruption in order to make counterfactual forecasts for the intervention period. We used our proposed methods to estimate the impact of the 2021 Texas six-week abortion ban on documented pregnancies among women in Texas while adjusting for the impact of the COVID-19 pandemic.