@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://aclanthology.org/2025.findings-naacl.427/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
%A Liu, Xiaoyu
%A Xu, Paiheng
%A Wu, Junda
%A Yuan, Jiaxin
%A Yang, Yifan
%A Zhou, Yuhang
%A Liu, Fuxiao
%A Guan, Tianrui
%A Wang, Haoliang
%A Yu, Tong
%A McAuley, Julian
%A Ai, Wei
%A Huang, Furong
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F liu-etal-2025-large-language
%X 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.
%R 10.18653/v1/2025.findings-naacl.427
%U https://aclanthology.org/2025.findings-naacl.427/
%U https://doi.org/10.18653/v1/2025.findings-naacl.427
%P 7668-7684
Markdown (Informal)
[Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey](https://aclanthology.org/2025.findings-naacl.427/) (Liu et al., Findings 2025)
ACL
- Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, and Furong Huang. 2025. Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7668–7684, Albuquerque, New Mexico. Association for Computational Linguistics.