Causality in Dynamical Systems
8In many real-world applications, predicting how a system reacts under an active perturbation is critical. Achieving this requires a robust causal methodology. While many causal methods and theoretical results have been developed for i.i.d. (independent and identically distributed) data, real-world data often arise from dynamical systems where temporal structure cannot be ignored.
This session highlights the need for adapting causal methodology to address these dynamic contexts. Using two examples, we demonstrate how leveraging the temporal structure of dynamical systems can uncover new possibilities. Specifically, we explore how these structures enable unique assumptions that help eliminate the effects of hidden confounding. We use this insight to separate the effects of internal variability and external forcing in Earth system science and to estimate price elasticities in the electricity market.
Speaker: Jonas Peters, Professor of Statistics, Department of Mathematics, ETH Zurich
The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.
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