  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.
