CLAIM-FUSE: Causal Discovery with Heterogeneous and Uncertain Knowledge

Causal discovery aims to recover cause-and-effect relationships from observational data, and large language models (LLMs) can help generate textual causal statements when data alone is not sufficient to identify the correct causal relationship. However, existing LLM-assisted methods either incur high computational costs by querying the causal relationship for every variable pair, or ask repeated questions despite some question types being more informative and more reliably answered than others. We therefore propose CLAIM-FUSE, which chooses which variables and which type of causal claim to query (directed edge, adjacency, ancestry, or order) at each step, minimizing the uncertainty within a fixed budget. Our empirical results show that CLAIM-FUSE achieves the lowest structural error among LLM-assisted methods, up to 19% below the strongest baseline, using orders of magnitude fewer tokens than exhaustive methods. Choosing among all claim types also improves recovery over querying only local or only ancestral claims, though it can degrade when the LLM is less reliable on the chosen type. Our work lays the foundation for knowledge elicitation that adapts not only the content, but also the type of the question.