DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data
Quick Answer
The DKCD framework enhances causal discovery from unstructured data in high-expertise domains by integrating domain knowledge, improving latent factor identification and causal graph accuracy.
Quick Take
The DKCD framework enhances causal discovery from unstructured data in high-expertise domains by integrating domain knowledge, improving latent factor identification and causal graph accuracy. Experiments show significant advancements in causal factor identification and graph construction over existing methods.
Key Points
- DKCD addresses challenges in causal discovery from unstructured data in healthcare, finance, and education.
- The framework includes Knowledge Mining, Knowledge-guided Causal Reasoning, and Causal Structure Discovery.
- Experiments demonstrate significant improvements in causal factor identification and graph construction.
- Existing methods struggle with latent factor identification and reliable annotation.
- DKCD leverages domain-specific knowledge to enhance causal reasoning.
Paper Resources
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~2 min readAbstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.09348 [cs.CL] |
| (or arXiv:2607.09348v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09348 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xin Li [view email]
[v1]
Fri, 10 Jul 2026 12:28:50 UTC (1,678 KB)
— Originally published at arxiv.org
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