3 papers
stat.ML2026
Coarsening Linear Non-Gaussian Causal Models with Cycles
Francisco Madaleno, Francisco C Pereira, Alex Markham
Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal struct…
cs.LG2026
Bayesian Hierarchical Invariant Prediction
Francisco Madaleno, Pernille Julie Viuff Sand, Francisco C. Pereira +1
We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (ICP) through the lens of Hierarchical Bayes. We leverage the hierarchical struct…
stat.ML2026
Coarsening Causal DAG Models
Francisco Madaleno, Pratik Misra, Alex Markham
Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across…