提出可解释的联邦贝叶斯因果推断框架，用于分布式制造系统中的因果效应分析。
Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
- 基于联邦贝叶斯学习估算本地参数后验，无需共享原始数据
- 在真实EHD打印数据上表现优于传统方法与主流联邦学习基准
- 适用于需隐私保护且要求结果可解释的工业制造场景
因果推断近年来在生物、医疗和环境科学等领域备受关注，尤其在可解释人工智能（xAI）系统中，用于揭示多变量间的因果关系。然而，在制造系统中尚未得到充分应用。本文提出一种可解释、可扩展且灵活的联邦贝叶斯学习框架 exttt{xFBCI}，旨在分布式制造系统中通过处理效应估计探索因果关系。利用联邦贝叶斯学习，高效估计各客户端局部参数的后验分布，从而获得每个客户端的倾向得分，而无需访问本地私有数据。这些得分随后用于倾向得分匹配（PSM）以估计处理效应。在多个数据集上的模拟实验及真实世界电液动力（EHD）打印数据验证表明，该方法在性能上超越标准贝叶斯因果推断方法及若干先进联邦学习基准。
原文摘要 · Abstract (English)
Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce an explainable, scalable, and flexible federated Bayesian learning framework, \texttt{xFBCI}, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and a real-world Electrohydrodynamic (EHD) printing data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks.
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