  Theoretical developments in sequential Bayesian analysis of multivariate
dynamic models underlie new methodology for causal prediction. This extends the
utility of existing models with computationally efficient methodology, enabling
routine exploration of Bayesian counterfactual analyses with multiple selected
time series as synthetic controls. Methodological contributions also define the
concept of outcome adaptive modelling to monitor and inferentially respond to
changes in experimental time series following interventions designed to explore
causal effects. The benefits of sequential analyses with time-varying parameter
models for causal investigations are inherited in this broader setting. A case
study in commercial causal analysis-- involving retail revenue outcomes related
to marketing interventions-- highlights the methodological advances.
