Compositional dynamic modelling for causal prediction in multivariate time series

TL;DR

This paper introduces a new Bayesian dynamic modeling approach for causal prediction in multivariate time series, enabling efficient counterfactual analysis and adaptive monitoring of causal effects, demonstrated through a retail marketing case study.

Contribution

It develops a computationally efficient Bayesian methodology for causal prediction in multivariate time series, incorporating outcome adaptive modeling and sequential analysis techniques.

Findings

Enhanced capability for causal inference in multivariate time series

Effective counterfactual analysis with synthetic controls

Application to retail revenue and marketing interventions

Abstract

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.