Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis

ChatGPT[3] has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can. Thus, we would like to push its limit and explore its ability to answer causal discovery questions by using a medical benchmark [5] in causal discovery. Causal discovery aims to uncover the underlying unknown causal relationships based purely on observational data[2]. In contrast, applying ChatGPT to answer the questions about causal relationships is fundamentally different. With the current version of ChatGPT, we can only use the names (meta information) instead of observational data of variables to answer causal questions. The answers to the causal questions given by ChatGPT are based on a trained large language model, which can be viewed as an approximation for existing knowledge in the training natural language data.