3 papers
stat.ML2025
Nonlinear Causal Discovery for Grouped Data
Konstantin Göbler, Tobias Windisch, Mathias Drton
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables. In many impo…
stat.ML2024
: Generating Realistic Production Data for Benchmarking Causal Discovery
Konstantin Göbler, Tobias Windisch, Mathias Drton +3
Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a…
stat.ML2024
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Konstantin Göbler, Anne Miloschewski, Mathias Drton +1
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well developed in…