## Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

#### Standard

**Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values.** / Weichwald, Sebastian; Emil Jakobsen, Martin; Mogensen, Phillip Bredahl; Petersen, Lasse; Thams, Nikolaj Theodor; Varando, Gherardo.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

#### Harvard

*Proceedings of the NeurIPS 2019 Competition and Demonstration Track.*PMLR, Proceedings of Machine Learning Research, vol. 123, pp. 27-36, Neural Information Processing Systems Conference 2019, , Vancouver, Canada, 08/12/2019. <http://proceedings.mlr.press/v123/weichwald20a/weichwald20a.pdf>

#### APA

*Proceedings of the NeurIPS 2019 Competition and Demonstration Track*(pp. 27-36). PMLR. Proceedings of Machine Learning Research Vol. 123 http://proceedings.mlr.press/v123/weichwald20a/weichwald20a.pdf

#### Vancouver

#### Author

#### Bibtex

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#### RIS

TY - GEN

T1 - Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

AU - Weichwald, Sebastian

AU - Emil Jakobsen, Martin

AU - Mogensen, Phillip Bredahl

AU - Petersen, Lasse

AU - Thams, Nikolaj Theodor

AU - Varando, Gherardo

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Article in proceedings

T3 - Proceedings of Machine Learning Research

SP - 27

EP - 36

BT - Proceedings of the NeurIPS 2019 Competition and Demonstration Track

PB - PMLR

T2 - Neural Information Processing Systems Conference 2019,

Y2 - 8 December 2019 through 14 December 2019

ER -

ID: 248233218