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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
OriginalsprogEngelsk
TitelProceedings of the NeurIPS 2019 Competition and Demonstration Track
ForlagPMLR
Publikationsdato2020
Sider27-36
StatusUdgivet - 2020
BegivenhedNeural Information Processing Systems Conference 2019, - Vancouver, Canada
Varighed: 8 dec. 201914 dec. 2019

Konference

KonferenceNeural Information Processing Systems Conference 2019,
LandCanada
ByVancouver
Periode08/12/201914/12/2019
NavnProceedings of Machine Learning Research
Vol/bind123
ISSN1938-7228

ID: 248233218