Causal Discovery With Metadata-Informed Latent Types

Résumé

Causal discovery seeks to recover causal structure from data but the underlying graph is typically identifiable only up to its Markov equivalence class. Yet real-world systems often exhibit redundancy, where groups of variables share similar causal roles. We introduce a Bayesian causal discovery framework that leverages variable-level metadata to infer latent types and constrain causal interactions across variables. We model causal graphs as type-consistent DAGs and propose t-DiBS, a fully differentiable method that jointly learns variable types, graph structure, and metadata representations. Our approach enables principled uncertainty quantification and integrates expressive neural models for metadata. We provide theoretical results showing that, under structured assumptions, metadata combined with typing can improve identifiability beyond classical limits. Empirically, we demonstrate improved performance over standard causal discovery methods on synthetic and pseudo-real datasets, including ablations demonstrating the benefit of joint type and structure learning. These results establish metadata-driven typing as a principled approach to identifiable causal discovery.