跨域多传感器系统中，用因果迁移学习识别共性因果机制。
Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
- 分三步：先找跨域共性机制，再针对各域补全个性机制。
- 在制造场景模拟中，性能优于单一数据池或单独分析方法。
- 适合需要跨领域理解传感器因果关系的研究者。
为从因果视角深入理解复杂传感器系统，本文提出一种名为共性与个体因果机制估计（CICME）的新方法，用于从多个领域异构数据中推断因果机制。基于因果迁移学习（CTL）原理，当样本充足时，CICME能可靠检测出跨域不变的因果机制，并利用这些共性机制指导各领域剩余因果机制的估计。在仿照制造过程的线性高斯模型场景下评估性能。相比现有基于连续优化的因果发现方法，CICME结合了对合并数据和各领域数据反复分析的优势，在特定条件下甚至超越两种基线方法。
原文摘要 · Abstract (English)
To gain deeper insights into a complex sensor system through the lens of causality, we present common and individual causal mechanism estimation (CICME), a novel three-step approach to inferring causal mechanisms from heterogeneous data collected across multiple domains. By leveraging the principle of Causal Transfer Learning (CTL), CICME is able to reliably detect domain-invariant causal mechanisms when provided with sufficient samples. The identified common causal mechanisms are further used to guide the estimation of the remaining causal mechanisms in each domain individually. The performance of CICME is evaluated on linear Gaussian models under scenarios inspired from a manufacturing process. Building upon existing continuous optimization-based causal discovery methods, we show that CICME leverages the benefits of applying causal discovery on the pooled data and repeatedly on data from individual domains, and it even outperforms both baseline methods under certain scenarios.
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