Inferring causation from time series in Earth system sciences

Runge, Jakob; Bathiany, Sebastian; Bollt, Erik; Camps-Valls, Gustau; Coumou, Dim; Deyle, Ethan; Glymour, Clark; Kretschmer, Marlene; Mahecha, Miguel D.; Muñoz-Marí, Jordi; van Nes, Egbert H.; Peters, Jonas; Quax, Rick; Reichstein, Markus; Scheffer, Marten; Schölkopf, Bernhard; Spirtes, Peter; Sugihara, George; Sun, Jie; Zhang, Kun; ... (2019). Inferring causation from time series in Earth system sciences. Nature Communications, 10(1) Springer Nature 10.1038/s41467-019-10105-3

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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:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Physics Institute > Climate and Environmental Physics
10 Strategic Research Centers > Oeschger Centre for Climate Change Research (OCCR)

UniBE Contributor:

Zscheischler, Jakob

Subjects:

500 Science > 530 Physics

ISSN:

2041-1723

Publisher:

Springer Nature

Funders:

[4] Swiss National Science Foundation

Projects:

[1295] New metrics for constraining multiple drivers of hazard and compound hazards Official URL

Language:

English

Submitter:

Jakob Zscheischler

Date Deposited:

25 Feb 2020 14:19

Last Modified:

05 Dec 2022 15:36

Publisher DOI:

10.1038/s41467-019-10105-3

BORIS DOI:

10.7892/boris.140028

URI:

https://boris.unibe.ch/id/eprint/140028

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