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.
翻译:对控制和经处理单位按顺序观测的时间序列数据进行客观评价,重点是利用合成控制结构进行干预的影响,方法贡献包括制定多种处理单位时间变化影响的多变动态模型和对连续学习干预效果的明显偏差。分析探讨了减少多种潜在合成控制变量的维度的效用。这些方法的进步在商业预测的详细案例研究中进行了评价。这包括在超市促销试验中研究评价干预措施,同时对大型商业系统选定区域进行预测分析。从实验环境到广大人口的因果关系预测推论是一个中心问题,一个可能受到跨系列依赖影响的问题。