Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
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Documents
- weichwald20a_Causal structure learning from time series_(publisher_version)
Final published version, 275 KB, PDF document
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS). We examine how our combination of established ideas achieves competitive performance on semi-realistic and realistic time series data exhibiting common challenges in real-world Earth sciences data. In particular, we discuss a) a rationale for leveraging linear methods to identify causal links in non-linear systems, b) a simulation-backed explanation as to why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. For benchmark usage, we detail the algorithms here and provide implementations at {https://github.com/sweichwald/tidybench}. We propose the presented competition-proven methods for baseline benchmark comparisons to guide the development of novel algorithms for structure learning from time series.
Original language | English |
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Title of host publication | Proceedings of the NeurIPS 2019 Competition and Demonstration Track |
Publisher | PMLR |
Publication date | 2020 |
Pages | 27-36 |
Publication status | Published - 2020 |
Event | Neural Information Processing Systems Conference 2019, - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 |
Conference
Conference | Neural Information Processing Systems Conference 2019, |
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Land | Canada |
By | Vancouver |
Periode | 08/12/2019 → 14/12/2019 |
Series | Proceedings of Machine Learning Research |
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Volume | 123 |
ISSN | 1938-7228 |
Links
- http://proceedings.mlr.press/v123/weichwald20a/weichwald20a.pdf
Final published version
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