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.
@article{arxiv.2303.05279,
title = {Can large language models build causal graphs?},
author = {Stephanie Long and Tibor Schuster and Alexandre Piché},
journal= {arXiv preprint arXiv:2303.05279},
year = {2024}
}
Comments
Peer reviewed and accepted for presentation at the Causal Machine Learning for Real-World Impact Workshop (CML4Impact) at NeuRIPs2022 Fixed author list