Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
Published in Proceedings of the NeurIPS 2019 Competition and Demonstration Track, Proceedings of Machine Learning Research (PMLR), 2020
Recommended citation: Sebastian Weichwald, Martin E. Jakobsen, Phillip B. Mogensen, Lasse Petersen, Nikolaj Thams, and Gherardo Varando (2020). Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:27-36. http://proceedings.mlr.press/v123/weichwald20a/weichwald20a.pdf
Abstract: 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.