用于因果预测的多元贝叶斯动态建模
Multivariate Bayesian dynamic modeling for causal prediction
AI总结：
本研究提出用于因果推断的多元贝叶斯动态建模方法，通过共轭模型实现高效序贯学习与不确定性量化，经超市促销实验验证，凸显跨处理组因果推断中恰当不确定性量化的重要性。
摘要：
贝叶斯预测在多元时间序列分析中被发展用于因果推断。对来自对照组和处理组的序列观测时间序列数据的因果评估，聚焦于利用对照组同期结果的干预措施的影响。本文的方法学进展涉及适用于多个处理组时变效应的多元动态模型，明确聚焦于干预效应的序贯学习与聚合。分析探索了多个合成反事实预测变量的降维方法。计算进展利用完全共轭模型实现高效的序贯学习与推断，包括跨单元相关性及其时变特性。这使得能够通过贝叶斯模型平均对模型超参数进行完整的不确定性量化。一项详细的案例研究评估了超市促销实验中的干预措施，并在大型商业系统的选定区域开展了耦合预测分析。与现有方法的比较凸显了在跨处理组聚合的因果推断中，恰当的不确定性量化等实际问题。
英文摘要：
Bayesian forecasting is developed in multivariate 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 contemporaneous outcomes in control units. Methodological developments here concern multivariate dynamic models for time-varying effects across multiple treated units with explicit foci on sequential learning and aggregation of intervention effects. Analysis explores dimension reduction across multiple synthetic counterfactual predictors. Computational advances leverage fully conjugate models for efficient sequential learning and inference, including cross-unit correlations and their time variation. This allows full uncertainty quantification on model hyper-parameters via Bayesian model averaging. A detailed case study evaluates interventions in a supermarket promotions experiment, with coupled predictive analyses in selected regions of a large-scale commercial system. Comparisons with existing methods highlight the issues of appropriate uncertainty quantification in casual inference in aggregation across treated units, among other practical concerns.
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