Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
This work addresses the problem of improving causal inference from time series data for researchers in fields like climate science, though it is incremental as it builds on existing ideas.
The authors tackled causal structure learning from time series data, particularly in Earth sciences, by combining established linear methods to achieve competitive performance on semi-realistic and realistic benchmarks, demonstrating that large regression coefficients can predict causal links better than small p-values in practice.
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.