Title: Language Models as Noisy Experts for Sequential Causal Discovery
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Causal Discovery, Sequential Data, Noisy Expert, Language Model

Abstract: Causal discovery from observational data typically assumes access to complete data and availability of domain experts. In practice, data often arrive in batches, are subject to sampling bias, and expert knowledge is scarce. Language Models (LMs) offer a surrogate for expert knowledge but suffer from hallucinations, inconsistencies, and bias. We present a hybrid framework that bridges these gaps by adaptively integrating sequential batch data with LM-derived noisy, expert knowledge while accounting for both *data-induced* and *LM-induced* biases. We propose a representation shift from Directed Acyclic Graph (DAG) to Partial Ancestral Graph (PAG), that accommodates ambiguities within a coherent framework, allowing grounding the *global* LM knowledge in *local* observational data. To guide LM interactions, we use a sequential optimization scheme that adaptively queries the most informative edges. Across varied datasets, we outperform prior work in structural accuracy and extend to parameter estimation, showing robustness to LM noise.