Data-Driven Augmentation of Expert Causal Knowledge in Cyber-Physical Systems

As industrial systems (such as smart grids) grow in size and complexity, transparency and explainability become increasingly important for supporting user understanding and trust. Causal models are key components to translate system behaviour into human reasoning. While causal graphs can be created from domain-expert knowledge about system dynamics, this method carries the risk of specifying only partially complete graphs. Yet, attempting to learn the entire causal graph from observational data is also known to be challenging and<br/>error-prone. In this paper, we propose a procedure for the data-driven augmentation of existing causal graphs defined by domain-experts. Specifically, we test Granger non-causalities implied by the existing graph on sensor measurement data. Under appropriate statistical and causal assumptions, the test results can then indicate missing edges in the existing graph. We evaluated our approach in a real-life smart charging garage scenario, testing the expert-defined causal graphs with real-world sensor data. The results show inconsistencies between the causal graph defined by experts and the observed data, demonstrating the capability of our approach to reveal missing causal relations. These findings highlight the potential of our approach in combining expert knowledge and data-driven analysis for validating and augmenting causal representations in complex systems, such as smart grids, contributing to the broader topic of transparent and interpretable industrial systems.