le 12 novembre 2025
Titre : Causality, why it matters in industrial context
Abstract: Causality plays a crucial role in critical systems monitoring, where understanding why something happened (or might happen) is far more important than merely knowing what happened.
In this work, I will present the basic principles of causal analysis in practice, with several examples of successful applications in energy forecasting, marketing or root cause detection in IoT, along with perspectives for future research and collaborations
Bibliography
C. Yvernes, E. Devijver, A. Ribeiro, M. Clausel, E. Gaussier, Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering. Conference on Neural Information Processing Systems. NeurIPS (2025)
M. Benhamza, M. Clausel, M. Tami. Counterfactual Robustness: a framework to analyze the robustness of Causal Generative Models across interventions. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML (2025)
E. Lu, C. Findling, M. Clausel, A. Leite, W. Gong, P. Kersaudy, Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction. Worshop BERT25, NeurIPS 2025.
D. Bouche, R. Flamary, F. d’Alché-Buc, R. Plougonven, M. Clausel, J. Badosa, P. Drobinski. Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection. Renewable Energy, vol 211 pp 938-947 (2023)
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