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…
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…
cs.LG2025
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…