Detecting causal associations in large nonlinear time series datasets

Journal article

Identifying causal relationships from observational time series data is a key problem in disciplines such as climate science or neuroscience, where experiments are often not possible. Data-driven causal inference is challenging since datasets are often high-dimensional and nonlinear with limited sample sizes. Here we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm that allows to reconstruct causal networks from large-scale time series datasets. We validate the method on a well-established climatic teleconnection connecting the tropical Pacific with extra-tropical temperatures and using large-scale synthetic datasets mimicking the typical properties of real data. The experiments demonstrate that our method outperforms alternative techniques in detection power from small to large-scale datasets and opens up entirely new possibilities to discover causal networks from time series across a range of research fields.

Authors: Runge, J; Nowack, P; Kretschmer, M; Flaxman, S; Sejdinovic, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Association for the Advancement of Science
• Journal: Science Advances
• Volume: 5
• Issue: 11
• Article number: eaau4996
• Publication date: 2019-11-27
• Acceptance date: 2019-08-19
• DOI: 10.1126/sciadv.aau4996
• ISSN: 2375-2548
• Keywords: FFR