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：贝叶斯动态建模与预测在序贯时间序列分析的因果推断框架下得到发展。针对控制组与处理组的序贯观测时间序列数据，因果评估聚焦于干预效应，采用综合控制构造方法。方法论创新包括：针对多个处理单元的时变效应开发多变量动态模型，并明确关注干预效应的序贯学习。研究探讨了多个潜在综合控制变量降维的实用性。这些方法论进展在商业预测的详细案例研究中得到验证，涉及超市促销实验中的干预效应评估，并耦合大规模商业系统选定区域的预测分析。从实验环境向更广泛群体推广因果预测推断是核心关注点，且可能受到跨序列依赖性的影响。