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

• URL: http://arxiv.org/abs/2310.18654v1
• Date: Sat, 28 Oct 2023 09:47:02 GMT
• Title: Causal discovery in a complex industrial system: A time series benchmark
• Authors: S{\o}ren Wengel Mogensen and Karin Rathsman and Per Nilsson
• Abstract summary: Causal discovery produces a causal structure, represented by a graph, from observed data. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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