Causal Discovery under Scaled Noise: Identifiability and Robust Estimation
Zürich, a postdoc fellow at the ETH AI Center, and part of the Medical Data Science Group. He did his PhD in the Exploratory Data Analysis group affiliated with the CISPA Helmholtz Center for Information [...] a professor at TU Dortmund, leading the Causality group at the Research Center for Trustworthy Data Science and Security and the Department of Statistics, and a member of the ELLIS society. His research [...] discovery aims to learn causal networks, i.e., directed acyclic graphs (DAGs), from observational data. Although the problem is non-identifiable in the most general form, we can achieve identifiability …
