Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
This work addresses the challenge of causal discovery in complex sensor systems for applications like manufacturing, but it is incremental as it builds on existing optimization-based methods.
The paper tackles the problem of inferring causal mechanisms from heterogeneous multi-sensor data across multiple domains by introducing CICME, a three-step approach that leverages causal transfer learning to identify domain-invariant mechanisms and guide individual domain estimations, showing it outperforms baseline methods in certain linear Gaussian model scenarios.
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.