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Session Overview |
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Plenary I
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9:15 am - 10:15 am
Causality in Dynamical Systems
Causal models can help us with the following two tasks: (1) they can predict how a real-world system reacts under an active perturbation; (2) they suggest ways to robustly predict a response variable under a distribution shift, that is, in a scenario, where training and test distributions differ. Many causal methods and theoretical results have been developed for settings where data follow an i.i.d. structure.
Often, however, data come from a dynamical system whose temporal structure cannot be ignored. In this talk, we argue that considering time-dependence does not only come with technical difficulties but also with benefits: we develop causal methods that do not have a direct correspondence in the i.i.d. world and show how they can be used for separating the effects of internal variability and external forcing in Earth system science, for example.
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