Causal discovery in a complex industrial system : A time series benchmark
(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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5d50fcee-7867-4379-a1cc-84d68f0ed181
- author
- Mogensen, Søren Wengel LU ; Rathsman, Karin LU and Nilsson, Per LU
- organization
- publishing date
- 2024
- 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}}, }