Detecting and quantifying causal associations in large nonlinear time series datasets-Reference-Cited by-同舟云学术

Detecting and quantifying causal associations in large nonlinear time series datasets

Author:

Runge Jakob¹²^ORCID,Nowack Peer²³⁴^ORCID,Kretschmer Marlene⁵^ORCID,Flaxman Seth⁴⁶,Sejdinovic Dino⁷⁸^ORCID

Affiliation:

1. German Aerospace Center, Institute of Data Science, 07745 Jena, Germany.

2. Grantham Institute, Imperial College, London SW7 2AZ, UK.

3. Department of Physics, Blackett Laboratory, Imperial College, London SW7 2AZ, UK.

4. Data Science Institute, Imperial College, London SW7 2AZ, UK.

5. Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.

6. Department of Mathematics, Imperial College, London SW7 2AZ, UK.

7. The Alan Turing Institute for Data Science, London NW1 3DB, UK.

8. Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.

Abstract

A novel causal discovery method for estimating nonlinear interdependency networks from large time series datasets.

Funder

James S. McDonnell Foundation

European Regional Development Fund

EPSRC

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference80 articles.

1. IPCC Climate Change 2013: The Physical Science Basis (Cambridge Univ. Press 2013).

4. J. Pearl Causality: Models Reasoning and Inference (Cambridge Univ. Press 2000).

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