AI for Good webinar: Causality in Dynamical Systems

In many real-world applications, predicting how a system reacts under an active perturbation is critical. Achieving this requires 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 arises 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.

Please register here: https://aiforgood.itu.int/event/causality-in-dynamical-systems/