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

Mogensen, Søren Wengel LU ; Rathsman, Karin LU and Nilsson, Per LU (2024) 3rd Conference on Causal Learning and Reasoning, CLeaR 2024 236. p.1218-1236
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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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
benchmark data, Causal discovery, causal graphs, European Spallation Source, time series
host publication
Proceedings of Machine Learning Research
editor
Locatello, F. and Didelez, V.
volume
236
pages
19 pages
publisher
ML Research Press
conference name
3rd Conference on Causal Learning and Reasoning, CLeaR 2024
conference location
Los Angeles, United States
conference dates
2024-04-01 - 2024-04-03
external identifiers
  • scopus:85193544727
language
English
LU publication?
yes
id
5d50fcee-7867-4379-a1cc-84d68f0ed181
date added to LUP
2024-06-14 10:45:20
date last changed
2024-06-14 10:46:18
@inproceedings{5d50fcee-7867-4379-a1cc-84d68f0ed181,
  abstract     = {{<p>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.</p>}},
  author       = {{Mogensen, Søren Wengel and Rathsman, Karin and Nilsson, Per}},
  booktitle    = {{Proceedings of Machine Learning Research}},
  editor       = {{Locatello, F. and Didelez, V.}},
  keywords     = {{benchmark data; Causal discovery; causal graphs; European Spallation Source; time series}},
  language     = {{eng}},
  pages        = {{1218--1236}},
  publisher    = {{ML Research Press}},
  title        = {{Causal discovery in a complex industrial system : A time series benchmark}},
  volume       = {{236}},
  year         = {{2024}},
}