Abstract: In recent decades, causal inference has been a significant research topic in various fields, including statistics, computer science, education, public policy, and economics. Most causal inference methods focus on the analysis of sample observational data and text corpora. However, with the emergence of various knowledge graphs and large language models, causal inference tailored to knowledge graphs and large models has gradually become a research hotspot. In this paper, different causal inference methods are classified based on their orientation towards sample observational data, text data, knowledge graphs, and large language models. Within each classification, this paper provides a detailed analysis of classical research works, including their problem definitions, solution methods, contributions, and limitations. Additionally, this paper places particular emphasis on discussing recent advancements in the integration of causal inference methods with knowledge graphs and large language models. Various causal inference methods are analyzed and compared from the perspectives of efficiency and cost, and specific applications of knowledge graphs and large language models in causal inference tasks are summarized. Finally, future development directions of causal inference in combination with knowledge graphs and large models are prospected.

Key words: causal relationship, knowledge graph, large language model

摘要： 近几十年来，因果关系推断是统计学、计算机科学、教育、公共政策和经济学等许多领域的一个重要研究课题。其中大部分因果推断方法是从样本观测数据和文本语料分析的角度进行研究。如今，随着各种知识图谱和大语言模型的涌现，面向知识图谱和大模型的因果关系推断逐渐成为了研究热点。因此，将不同的因果关系推断方法按照面向样本观测数据、文本数据、知识图谱和大语言模型进行分类，在每个分类中，对经典的研究工作从其问题定义、解决方法、贡献和不足进行了细致的分析。同时，对因果关系推断方法与知识图谱和大语言模型相结合的研究进展进行了重点讨论。从效率和成本角度分析和比较了不同因果推断方法，总结归纳了知识图谱和大语言模型在因果关系推断任务中的具体应用。最后，对知识图谱和大模型相结合的因果关系推断的未来发展方向进行了展望。

关键词: 因果关系, 知识图谱, 大语言模型