DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data

Researchers introduced DKCD, a framework that enhances causal discovery from unstructured data in high-expertise domains like healthcare and finance by integrating domain knowledge with large language models. The framework addresses limitations in identifying latent causal factors and improving annotation reliability, outperforming existing methods on domain-specific datasets.

arXiv:2607.09348v1 Announce Type: new Abstract: Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise domains such as healthcare, finance, and education. Existing methods typically leverage the general knowledge of large language models LLMs to identify causal factors from unstructured data and annotate them into structured data for causal graph construction. However, they remain limited by two key challenges CHs : CH1 insufficient identification of latent factors, which are implicit in the data yet essential for causal discovery, due to the lack of domain-specific knowledge; and CH2 unreliable factor annotation, caused by the lack of domain-grounded reasoning, which propagates errors to the resulting causal graphs. To address these challenges, we introduce a novel Domain Knowledge-enhanced Causal Discovery framework DKCD for causal discovery from unstructured data in high-expertise domains with three interconnected components: 1 Knowledge Mining: It retrieves relevant domain knowledge based on observable factors to support subsequent causal reasoning. 2 Knowledge-guided Causal Reasoning: Reasoning with relevant knowledge, it discovers latent causal factors to address CH1 and generates key causal clues for more accurate data annotation to address CH2. 3 Causal Structure Discovery: It constructs the final causal graphs based on a more complete factor set and accurate annotations. Experiments on two domain-specific datasets show that DKCD significantly improves both causal factor identification and causal graph construction.