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 Security [...] 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 [...] focus on the estimation of Gaussian LSNMs (heteroscedastic regression) on moderately high dimensional data via deep neural networks. Connecting to prior work on Bayesian inference, we propose an efficient …