Causal Representation Learning from Multi-modal Biomedical Observations.

ABSTRACT: Prevalent in biomedical applications (e.g., human phenotype research), multi-modal datasets can provide valuable insights into the underlying physiological mechanisms. However, current machine learning (ML) models designed to analyze these datasets often lack interpretability and identifiability guarantees, which are essential for biomedical research. Recent advances in causal representation learning have shown promise in identifying interpretable latent causal variables with formal theoretical guarantees. Unfortunately, most current work on multi-modal distributions either relies on restrictive parametric assumptions or yields only coarse identification results, limiting their applicability to biomedical research that favors a detailed understanding of the mechanisms. In this work, we aim

SUBMITTER: Sun Y 

PROVIDER: S-EPMC11952583 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature