Building causal graphs can be a laborious process. To ensure all relevant causal pathways have been captured, researchers often have to discuss with clinicians and experts while also reviewing extensive relevant medical literature. By encoding common and medical knowledge, large language models (LLMs) represent an opportunity to ease this process by automatically scoring edges (i.e., connections between two variables) in potential graphs. LLMs however have been shown to be brittle to the choice of probing words, context, and prompts that the user employs. In this work, we evaluate if LLMs can be a useful tool in complementing causal graph development.

翻译：构建因果图可能是一项费力的过程。为了确保所有相关因果路径已被捕获，研究者通常需要与临床医生和专家讨论，同时还要回顾大量相关医学文献。大型语言模型（LLMs）通过编码常识和医学知识，为自动对潜在图中的边（即两个变量之间的连接）进行评分提供了简化这一过程的机遇。然而，研究表明大型语言模型对用户使用的探测词、上下文和提示的选择较为敏感。在本研究中，我们评估了大型语言模型能否成为补充因果图开发的有用工具。