Publications
Selected publications
Nonlinear Causal Discovery for Grouped Data
June 5, 2025
Inferring cause-effect relationships from observational data has gained significant attention in recent years, but most methods are limited to scalar random variables.
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
May 15, 2024
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 the case where all variables are either continuous or discrete, including in high dimensions.
causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery
April 1, 2024
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 need for adequate empirical validation of the causal relationships learned by different algorithms.