Multivariate Bayesian dynamic modeling for causal prediction
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Bayesian dynamic modeling and forecasting is developed in the setting of sequential time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated units focuses on the impacts of interventions using synthetic control constructs. Methodological contributions include the development of multivariate dynamic models for time-varying effects across multiple treated units and explicit foci on sequential learning of effects of interventions. Analysis explores the utility of dimension reduction of multiple potential synthetic control variables. These methodological advances are evaluated in a detailed case study in commercial forecasting. This involves in-study evaluation of interventions in a supermarket promotions experiment, with coupled predictive analyses in selected regions of a large-scale commercial system. Generalization of causal predictive inferences from experimental settings to broader populations is a central concern, and one that can be impacted by cross-series dependencies.
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