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In this article, we describe the algorithms for causal structure learningfrom time series data that won the Causality 4 Climate competition at theConference on Neural Information Processing Systems 2019 (NeurIPS). We examinehow our combination of established ideas achieves competitive performance onsemi-realistic and realistic time series data exhibiting common challenges inreal-world Earth sciences data. In particular, we discuss a) a rationale forleveraging linear methods to identify causal links in non-linear systems, b) asimulation-backed explanation as to why large regression coefficients maypredict causal links better in practice than small p-values and thus whynormalising the data may sometimes hinder causal structure learning.  For benchmark usage, we detail the algorithms here and provideimplementations at https://github.com/sweichwald/tidybench . We propose thepresented competition-proven methods for baseline benchmark comparisons toguide the development of novel algorithms for structure learning from timeseries.

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