Causal discovery in a complex industrial system: A time series benchmark
This work addresses a critical bottleneck for researchers in causal discovery by offering a realistic testbed, though it is incremental as it focuses on a specific domain rather than advancing the core methodology.
The paper tackles the challenge of evaluating causal discovery methods on real-world time series data by introducing a dataset from an industrial subsystem at the European Spallation Source, complete with an expert-constructed causal graph, providing a benchmark for testing and improving these methods.
Causal discovery outputs a causal structure, represented by a graph, from observed data. For time series data, there is a variety of methods, however, it is difficult to evaluate these on real data as realistic use cases very rarely come with a known causal graph to which output can be compared. In this paper, we present a dataset from an industrial subsystem at the European Spallation Source along with its causal graph which has been constructed from expert knowledge. This provides a testbed for causal discovery from time series observations of complex systems, and we believe this can help inform the development of causal discovery methodology.