Causal reasoning ability is crucial for numerous NLP applications. Despite
the impressive emerging ability of ChatGPT in various NLP tasks, it is unclear
how well ChatGPT performs in causal reasoning. In this paper, we conduct the
first comprehensive evaluation of the ChatGPT's causal reasoning capabilities.
Experiments show that ChatGPT is not a good causal reasoner, but a good causal
interpreter. Besides, ChatGPT has a serious hallucination on causal reasoning,
possibly due to the reporting biases between causal and non-causal
relationships in natural language, as well as ChatGPT's upgrading processes,
such as RLHF. The In-Context Learning (ICL) and Chain-of-Though (COT)
techniques can further exacerbate such causal hallucination. Additionally, the
causal reasoning ability of ChatGPT is sensitive to the words used to express
the causal concept in prompts, and close-ended prompts perform better than
open-ended prompts. For events in sentences, ChatGPT excels at capturing
explicit causality rather than implicit causality, and performs better in
sentences with lower event density and smaller lexical distance between events.

本文对 ChatGPT 的因果推理能力进行了全面评估。实验结果表明，ChatGPT 在因果推理方面存在严重的幻觉，主要由于自然语言中因果关系和非因果关系之间的汇报偏差以及 ChatGPT 的升级过程。此外，在提示中使用的表达因果概念的单词对 ChatGPT 的因果推理能力产生重要影响，针对性较强的提示表现更好。ChatGPT 主要擅长捕捉显式的因果关系，对隐式的因果关系处理能力相对较差，对句子中事件密度较低和事件之间词汇距离较小的情况处理效果较好。