Inferring causation from time series in Earth system sciences

Runge, J. ORCID: https://orcid.org/0000-0002-0629-1772, Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M. ORCID: https://orcid.org/0000-0002-2756-9526, Mahecha, M. D. ORCID: https://orcid.org/0000-0003-3031-613X, Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J. ORCID: https://orcid.org/0000-0003-2553-1804, Zhang, K. and Zscheischler, J. ORCID: https://orcid.org/0000-0001-6045-1629 (2019) Inferring causation from time series in Earth system sciences. Nature Communications, 10 (1). 2553. ISSN 2041-1723 doi: 10.1038/s41467-019-10105-3

Abstract/Summary

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/98537
Identification Number/DOI	10.1038/s41467-019-10105-3
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Nature Publishing Group
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