Prof. Dr. Jonas Peters
Causality: Models, Learning, and Invariance
In science, we often want to understand how a system reacts under interventions (e.g., under gene knock-out experiments or a change of policy). These questions go beyond statistical dependences and can therefore not be answered by standard regression or classification techniques. In this tutorial we will learn about the powerful language of causality and recent developments in the field. No prior knowledge about causality is required.
More precisely, we introduce structural causal models and formalize interventional distributions. We define causal effects and show how to compute them if the causal structure is known. We discuss assumptions under which causal structure becomes identifiable from observational (and interventional) data and describe corresponding methodology. If time allows, we present connections between causality and distributional robustness.
Biography: Jonas is a professor in statistics at the Department of Mathematical Sciences at the University of Copenhagen. Previously, he has worked at the Max-Planck-Institute for Intelligent Systems in Tuebingen and at the Seminar for Statistics, ETH Zurich. He studied Mathematics at the University of Heidelberg and the University of Cambridge. In his research, Jonas aims to infer causal relationships from different types of data and to build statistical methods that are robust with respect to distributional shifts. He seeks to combine theory, methodology, and applications (for example, in Earth system science and biology). His methodological work relates to areas such as computational statistics, causal inference, graphical models, high-dimensional statistics, and statistical testing. Jonas has received several awards, such as the Guy Medal in Bronze, the Silver Medal of the Royal Danish Academy of Sciences and Letters, and the ASA Causality in Statistics Education Award. Since 2021, he is a member of the COPSS Leadership academy.