Tuesday, 1st of October
(Shannon amphitheatre, 660 building) (see location
(German Aerospace Center, Institute of Data Science, Jena)
Perspectives for causal inference on time series in Earth system sciences
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In disciplines dealing with complex dynamical systems, such as the Earth system, replicated real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated time series data opens up the use of observational causal inference methods beyond the commonly adopted correlation techniques. Observational causal inference is a rapidly growing field with enormous potential to help answer long-standing scientific questions. Unfortunately, many methods are still little known and therefore rarely adopted in Earth system sciences. In this talk I will present a Perspective Paper in Nature Communications which identifies key generic problems and major challenges where causal methods have the potential to advance the state-of-the-art in Earth system sciences. I will also present a novel causal inference benchmark platform that aims to assess the performance of causal inference methods and to help practitioners choose the right method for a particular problem. Some recent methods that address particular challenges of Earth system data will be discussed and illustrated by application examples where causal methods have already led to novel insights in Earth sciences.
Runge, J., S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, J. Muñoz-Marı́, E. H. van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler (2019). Inferring causation from time series in earth system sciences. Nature Communications 10 (1), 2553.
Contact: guillaume.charpiat at inria.fr
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