Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
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.
Cite
@article{arxiv.2002.09573,
title = {Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values},
author = {Sebastian Weichwald and Martin E Jakobsen and Phillip B Mogensen and Lasse Petersen and Nikolaj Thams and Gherardo Varando},
journal= {arXiv preprint arXiv:2002.09573},
year = {2020}
}