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.

翻译：多元动态模型的序贯贝叶斯分析理论进展为因果预测提供了新的方法论基础。该方法通过计算高效的技术扩展了现有模型的实用性，使得以多个选定时间序列作为合成对照的贝叶斯反事实分析能够成为常规探索手段。方法论贡献还定义了结果自适应建模的概念，用于监测并推断实验时间序列在旨在探索因果效应的干预措施后发生的变化。时变参数模型在因果研究中进行序贯分析的优势在这一更广泛的框架中得以继承。一项关于商业因果分析的案例研究——涉及与营销干预相关的零售收入结果——突显了该方法的进展。