CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery 事件

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes. While large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference, existing LLM-augmented methods ar