@inproceedings{liu-etal-2025-large-language,
    title = "Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey",
    author = "Liu, Xiaoyu  and
      Xu, Paiheng  and
      Wu, Junda  and
      Yuan, Jiaxin  and
      Yang, Yifan  and
      Zhou, Yuhang  and
      Liu, Fuxiao  and
      Guan, Tianrui  and
      Wang, Haoliang  and
      Yu, Tong  and
      McAuley, Julian  and
      Ai, Wei  and
      Huang, Furong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.427/",
    pages = "7668--7684",
    ISBN = "979-8-89176-195-7",
    abstract = "Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables. The rise of generative Large Language Models (LLMs) has greatly impacted various language processing tasks. This survey focuses on research that evaluates or improves LLMs from a causal view in the following areas: reasoning capacity, fairness and safety issues, explainability, and handling multimodality. Meanwhile, LLMs can assist in causal inference tasks, such as causal relationship discovery and causal effect estimation, by leveraging their generation ability and knowledge learned during pre-training. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and robust artificial intelligence systems."
}