Research ArticleRESEARCH METHODS
Detecting and quantifying causal associations in large nonlinear time series datasets
- 1German Aerospace Center, Institute of Data Science, 07745 Jena, Germany.
- 2Grantham Institute, Imperial College, London SW7 2AZ, UK.
- 3Department of Physics, Blackett Laboratory, Imperial College, London SW7 2AZ, UK.
- 4Data Science Institute, Imperial College, London SW7 2AZ, UK.
- 5Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
- 6Department of Mathematics, Imperial College, London SW7 2AZ, UK.
- 7The Alan Turing Institute for Data Science, London NW1 3DB, UK.
- 8Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
- ↵*Corresponding author. Email: jakob.runge{at}dlr.de
↵† Present address: Department of Meteorology, University of Reading, Whiteknights Road, Reading RG6 6BG, UK.
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Science Advances 27 Nov 2019:
Vol. 5, no. 11, eaau4996
DOI: 10.1126/sciadv.aau4996
Vol. 5, no. 11, eaau4996
DOI: 10.1126/sciadv.aau4996
Jakob Runge
German Aerospace Center, Institute of Data Science, 07745 Jena, Germany.
Grantham Institute, Imperial College, London SW7 2AZ, UK.
Peer Nowack
Grantham Institute, Imperial College, London SW7 2AZ, UK.
Department of Physics, Blackett Laboratory, Imperial College, London SW7 2AZ, UK.
Data Science Institute, Imperial College, London SW7 2AZ, UK.
Marlene Kretschmer
Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
Seth Flaxman
Data Science Institute, Imperial College, London SW7 2AZ, UK.
Department of Mathematics, Imperial College, London SW7 2AZ, UK.
Dino Sejdinovic
The Alan Turing Institute for Data Science, London NW1 3DB, UK.
Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
