Causal discovery is becoming a key part in medical AI research. These methods
can enhance healthcare by identifying causal links between biomarkers,
demographics, treatments and outcomes. They can aid medical professionals in
choosing more impactful treatments and strategies. In parallel, Large Language
Models (LLMs) have shown great potential in identifying patterns and generating
insights from text data. In this paper we investigate applying LLMs to the
problem of determining the directionality of edges in causal discovery.
Specifically, we test our approach on a deidentified set of Non Small Cell Lung
Cancer(NSCLC) patients that have both electronic health record and genomic
panel data. Graphs are validated using Bayesian Dirichlet estimators using
tabular data. Our result shows that LLMs can accurately predict the
directionality of edges in causal graphs, outperforming existing
state-of-the-art methods. These findings suggests that LLMs can play a
significant role in advancing causal discovery and help us better understand
complex systems.

应用大型语言模型 (LLMs) 对因果发现中边的方向性进行预测，我们研究表明，LLMs 可以准确地预测因果图中边的方向性，并优于现有的最先进方法，这一发现表明 LLMs 在推进因果发现并帮助我们更好地理解复杂系统方面具有重要作用。