Detecting and quantifying causal associations in large nonlinear time series datasets

Runge, J., Nowack, P., Kretschmer, M. ORCID: https://orcid.org/0000-0002-2756-9526, Flaxman, S. and Sejdinovic, D. (2019) Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5 (11). eaau4996. ISSN 2375-2548 doi: 10.1126/sciadv.aau4996

Abstract/Summary

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems 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 to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/89762
Identification Number/DOI	10.1126/sciadv.aau4996
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Association for the Advancement of Science
Download/View statistics	View download statistics for this item