Large Language Models and Causal Inference in Collaboration: A
  Comprehensive Survey
Xiaoyu Liu Paiheng Xu Junda Wu Jiaxin Yuan Yifan Yang Yuhang Zhou Fuxiao Liu Tianrui Guan Haoliang Wang Tong Yu Julian McAuley Wei Ai Furong Huang
Causal inference has shown potential in enhancing the predictive accuracy,
fairness, robustness, and explainability of Natural Language Processing (NLP)
models by capturing causal relationships among variables. The emergence of
generative Large Language Models (LLMs) has significantly impacted various NLP
domains, particularly through their advanced reasoning capabilities. This
survey focuses on evaluating and improving LLMs from a causal view in the
following areas: understanding and improving the LLMs' reasoning capacity,
addressing fairness and safety issues in LLMs, complementing LLMs with
explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning
capacities can in turn contribute to the field of causal inference by aiding
causal relationship discovery and causal effect estimations. 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 equitable artificial intelligence systems.