3/3 Another highlight: A decision diagram to find the right approach for your problem. Great joint work with Andreas Gerhardus, Gherardo Varando, Veronika Eyring, and Gustau Camps-Valls. Integrate causal thinking into your #MachineLearning data-driven science!

2/2 The paper also introduces Mapped-PCMCI, a spatio-temporal causal discovery method to reconstruct causal networks from gridded data: Going from correlation to causal networks utilizing the assumption of a lower-dimensional latent causal process.

PS: We now have *flexible home office rules* at DLR enabling you to partially also work from another city than Jena.

2/2 Interested to develop #Causality and #AI theory and methods and work with climate scientists to better understand climate change and extremes? Join our team at http://climateinformaticslab.com