用大模型提升因果推断能力，解决医学经济等领域难题
Causal Inference with Large Language Model: A Survey
- 利用大语言模型融合人类知识与数据，改进因果推断方法
- 系统梳理了多类因果任务的进展与评估结果
- 适合对因果推断和大模型交叉研究感兴趣的学者
因果推断在医学、经济学等多个领域具有关键挑战，需结合人类知识、数学推理与数据挖掘。近年来自然语言处理的发展，尤其是大语言模型（LLMs）的出现，为传统因果推断任务带来了新机遇。本文综述了近期将大语言模型应用于因果推断的研究进展，涵盖不同因果层次的多种任务。我们总结了主要因果问题与方法，并对比了各类方法在不同因果场景下的评估结果。此外，讨论了关键发现并指明未来研究方向，强调了大模型在推动因果推断方法学发展中的潜在影响。
原文摘要 · Abstract (English)
Causal inference has been a pivotal challenge across diverse domains such as medicine and economics, demanding a complicated integration of human knowledge, mathematical reasoning, and data mining capabilities. Recent advancements in natural language processing (NLP), particularly with the advent of large language models (LLMs), have introduced promising opportunities for traditional causal inference tasks. This paper reviews recent progress in applying LLMs to causal inference, encompassing various tasks spanning different levels of causation. We summarize the main causal problems and approaches, and present a comparison of their evaluation results in different causal scenarios. Furthermore, we discuss key findings and outline directions for future research, underscoring the potential implications of integrating LLMs in advancing causal inference methodologies.
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