  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.
