Causality-Inspired Machine Learning: A Path to more Robust AI Systems
The pursuit of reliable and robust machine learning has become a central focus, with statistics and data science playing pivotal roles in its advancement. We explore connections between distributional robustness and causality, providing methodological insights to enhance the reliability of AI systems. We examine the broader implications of these concepts through a case study in digital health and conclude by emphasizing the importance of rigorous real-world validation of machine learning and AI algorithms.
Peter Bühlmann is Professor of Mathematics and Statistics and Director of Foundations of Data Science at ETH Zürich. He received his Ph.D. from ETH Zürich in 1993, and after spending three years as a postdoctoral fellow and Neyman Assistant Professor at UC Berkeley, he returned to ETH Zürich as a faculty member in 1997. His research interests include high-dimensional statistics, causality, and interdisciplinary applications in biomedical sciences. He is a Fellow of the Institute of Mathematical Statistics (IMS) and served as IMS President in 2022-2023, a Fellow of the American Statistical Association, and he was Co-Editor of the Annals of Statistics 2010-2012. He received a Doctor Honoris Causa from the Université Catholique de Louvain in 2017, the Neyman Lectureship and Award 2018 and the Wald Lectureship and Award 2024 from the Institute of Mathematical Statistics, the Guy Medal in Silver 2018 from the Royal Statistical Society, and he is an elected Member of the German National Academy of Sciences Leopoldina since 2022.