Detecting and quantifying causal associations in large nonlinear time series datasets

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Science Advances  27 Nov 2019:
Vol. 5, no. 11, eaau4996
DOI: 10.1126/sciadv.aau4996

Article Information

vol. 5 no. 11

Online ISSN: 
  • Received for publication June 16, 2018
  • Accepted for publication September 17, 2019
  • .

Author Information

  1. Jakob Runge1,2,*,
  2. Peer Nowack2,3,4,
  3. Marlene Kretschmer5,,
  4. Seth Flaxman4,6 and
  5. Dino Sejdinovic7,8
  1. 1German Aerospace Center, Institute of Data Science, 07745 Jena, Germany.
  2. 2Grantham Institute, Imperial College, London SW7 2AZ, UK.
  3. 3Department of Physics, Blackett Laboratory, Imperial College, London SW7 2AZ, UK.
  4. 4Data Science Institute, Imperial College, London SW7 2AZ, UK.
  5. 5Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
  6. 6Department of Mathematics, Imperial College, London SW7 2AZ, UK.
  7. 7The Alan Turing Institute for Data Science, London NW1 3DB, UK.
  8. 8Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
  1. *Corresponding author. Email: jakob.runge{at}
  • Present address: Department of Meteorology, University of Reading, Whiteknights Road, Reading RG6 6BG, UK.


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