We assess the ability of large language models (LLMs) to answer causal
questions by analyzing their strengths and weaknesses against three types of
causal question. We believe that current LLMs can answer causal questions with
existing causal knowledge as combined domain experts. However, they are not yet
able to provide satisfactory answers for discovering new knowledge or for
high-stakes decision-making tasks with high precision. We discuss possible
future directions and opportunities, such as enabling explicit and implicit
causal modules as well as deep causal-aware LLMs. These will not only enable
LLMs to answer many different types of causal questions for greater impact but
also enable LLMs to be more trustworthy and efficient in general.

我们分析了大型语言模型（LLMs) 回答因果问题的能力以及它们在三种类型的因果问题中的优势和劣势。我们认为，目前的 LLMs 可以结合现有的因果知识回答因果问题，但它们还不能满足于发现新的知识或高精度的决策任务。我们探讨了可能的未来方向和机会，例如启用显式和隐式因果模块以及深度因果感知 LLMs。这些不仅能够使 LLMs 回答更多不同类型的因果问题，也能使 LLMs 在一般情况下更加值得信赖和高效。