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.
@article{arxiv.2310.18654,
title = {Causal discovery in a complex industrial system: A time series benchmark},
author = {Søren Wengel Mogensen and Karin Rathsman and Per Nilsson},
journal= {arXiv preprint arXiv:2310.18654},
year = {2023}
}