Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning

What this work does

LinADMG connects identifiable representation learning with causal discovery under latent confounding. It uses variation in noise distributions across domains to recover directed structure and, under an additional no-cancellation condition, the full mixed graph.