Compositional Dynamic Modelling for Counterfactual Prediction in Multivariate Time Series

ABSTRACT Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for counterfactual prediction. This extends the utility of existing models with computationally efficient methodology, enabling routine exploration of post‐intervention analyses with multiple time series in putatively casual studies. Methodological contributions also define the concept of outcome adaptive modelling to monitor and respond to changes in experimental time series following interventions. The benefits of sequential analyses with time‐varying parameter models for such investigations are inherited in this broader setting. A case study in forecasting retail revenue following marketing interventions highlights the methodological advances.