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

Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Proceedings of the Third Conference on Causal Learning and Reasoning, PMLR 236:1218-1236, 2024.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v236-mogensen24a, title = {Causal discovery in a complex industrial system: A time series benchmark}, author = {Mogensen, S{\o}ren Wengel and Rathsman, Karin and Nilsson, Per}, booktitle = {Proceedings of the Third Conference on Causal Learning and Reasoning}, pages = {1218--1236}, year = {2024}, editor = {Locatello, Francesco and Didelez, Vanessa}, volume = {236}, series = {Proceedings of Machine Learning Research}, month = {01--03 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v236/mogensen24a/mogensen24a.pdf}, url = {https://proceedings.mlr.press/v236/mogensen24a.html}, 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.} }
Endnote
%0 Conference Paper %T Causal discovery in a complex industrial system: A time series benchmark %A Søren Wengel Mogensen %A Karin Rathsman %A Per Nilsson %B Proceedings of the Third Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2024 %E Francesco Locatello %E Vanessa Didelez %F pmlr-v236-mogensen24a %I PMLR %P 1218--1236 %U https://proceedings.mlr.press/v236/mogensen24a.html %V 236 %X 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.
APA
Mogensen, S.W., Rathsman, K. & Nilsson, P.. (2024). Causal discovery in a complex industrial system: A time series benchmark. Proceedings of the Third Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 236:1218-1236 Available from https://proceedings.mlr.press/v236/mogensen24a.html.

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