From Measurements to Concepts: LLM-Guided Latent Concept Causal Discovery for Medical Data

Causal discovery provides a structural basis for intervention and counterfactual analysis, making it valuable for understanding complex disease processes from observational medical data. However, medical datasets often contain multiple measurements reflecting shared but unobserved clinical states. Existing latent-variable causal discovery methods primarily search for hidden structure within predefined statistical model classes, but do not explicitly determine the appropriate level of causal abstraction. We formulate this problem as causal representation selection, coupling causal representation selection with causal graph learning. We propose LLCD, a framework explicitly addresses latent-aware causal discovery from this representation-selection perspective. LLCD uses LLM-derived semantics to propose candidate latent concepts, learns them with a missing-aware variational model, and selects among alternative latent–observed representations using held-out structural adequacy. We evaluate LLCD on semi-synthetic benchmarks with known latent causal structure and real-world ADNI and PPMI cohorts, assessing latent concept recovery, causal structure recovery, and causal interventional analysis. On the semi-synthetic benchmarks, LLCD improves directed F1 by \(66.0%\) and \(63.5%\) over the strongest baselines on ADNI and PPMI, respectively. LLCD also reduces causal-effect NMAE by approximately \(20%\) on both datasets. The code has been made public.