Causal discovery in a complex industrial system: A time series benchmark

Søren Wengel Mogensen; Karin Rathsman; Per Nilsson

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arXiv · 2310.18654

Causal discovery in a complex industrial system: A time series benchmark

Søren Wengel Mogensen·Karin Rathsman·Per Nilsson

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Abstract

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.

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Søren Wengel Mogensen, Karin Rathsman, Per Nilsson. 2023-10-28. Causal discovery in a complex industrial system: A time series benchmark. https://arxiv.org/abs/2310.18654

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