基于大语言模型的因果推断：综述
Causal Inference with Large Language Model: A Survey
AI总结：
本文综述了大语言模型在因果推断中的应用，梳理了不同因果层次上的主要任务、方法及评估结果，并展望了未来研究方向。
摘要：
因果推断在医学和经济学等多个领域中一直是一个关键挑战，需要将人类知识、数学推理和数据挖掘能力进行复杂的整合。自然语言处理（NLP）的最新进展，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的机遇。本文回顾了将大语言模型应用于因果推断的最新进展，涵盖跨越不同因果层次的多种任务。我们总结了主要的因果问题和方法，并比较了它们在不同因果场景下的评估结果。此外，我们讨论了关键发现并概述了未来研究方向，强调了整合大语言模型在推进因果推断方法论方面的潜在意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
