This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence. Despite the proficiency of LLMs in a range of tasks, their potential for understanding causality requires further exploration. We propose a novel causal attribution model that utilizes "do-operators" for constructing counterfactual scenarios, allowing us to systematically quantify the influence of input numerical data and LLMs' pre-existing knowledge on their causal reasoning processes. Our newly developed experimental setup assesses LLMs' reliance on contextual information and inherent knowledge across various domains. Our evaluation reveals that LLMs' causal reasoning ability depends on the context and domain-specific knowledge provided, and supports the argument that "knowledge is, indeed, what LLMs principally require for sound causal reasoning". On the contrary, in the absence of knowledge, LLMs still maintain a degree of causal reasoning using the available numerical data, albeit with limitations in the calculations.

翻译：本文探讨了大型语言模型（LLMs）的因果推理能力，以增强其在推进人工智能中的可解释性与可靠性。尽管LLMs在诸多任务中表现出色，但其理解因果关系的潜力仍需进一步探索。我们提出了一种新颖的因果归因模型，该模型利用"do-算子"构建反事实场景，从而能够系统性地量化输入数值数据及LLMs先验知识对其因果推理过程的影响。我们新开发的实验框架评估了LLMs在不同领域中对上下文信息与固有知识的依赖程度。评估结果表明，LLMs的因果推理能力依赖于所提供的上下文和领域特定知识，这支持了"知识确实是LLMs实现稳健因果推理的主要需求"这一论点。相反，在缺乏知识的情况下，LLMs仍能利用可用数值数据保持一定程度的因果推理能力，尽管其计算存在局限性。