Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
将大语言模型融入因果发现：统计因果方法AI 解读 ↓
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将大语言模型融入因果发现：统计因果方法
- 研究问题
- 如何把大语言模型提供的背景知识系统地纳入统计因果发现，并评估这种自动知识补充对因果结构恢复的帮助与错误风险？
- 方法与思路
- 作者结合面向语言模型的统计因果提示和面向因果发现算法的先验知识增强，并通过模拟、真实数据及成功与失败场景评估。
- 材料陈述的贡献
- 摘要称语言模型知识及统计因果提示改善了所考察实验中的结构恢复，也报告未公开数据上的改善，并讨论专家检查与关键错误风险。
- 阅读关注点
- 应核对先验约束的生成和冲突处理、真实因果结构的评估依据、训练数据未覆盖的证据，以及失败案例和专家复核流程。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收状态未核实。摘要未说明识别假设、误差指标和实验细节，语言模型知识不能直接视为因果证据。
原文摘要
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad knowledge of domain experts, despite the challenges in the systematic acquisition of background knowledge. To overcome these challenges, this paper proposes a novel method for causal inference, in which SCD and knowledge-based causal inference (KBCI) with a large language model (LLM) are synthesized through ``statistical causal prompting (SCP)'' for LLMs and prior knowledge augmentation for SCD. The experiments in this work have revealed that the results of LLM-KBCI and SCD augmented with LLM-KBCI approach the ground truths, more than the SCD result without prior knowledge. These experiments have also revealed that the SCD result can be further improved if the LLM undergoes SCP. Furthermore, with an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve the SCD on this dataset, even if this dataset has never been included in the training data of the LLM. For future practical application of this proposed method across important domains such as healthcare, we also thoroughly discuss the limitations, risks of critical errors, expected improvement of techniques around LLMs, and realistic integration of expert checks of the results into this automatic process, with SCP simulations under various conditions both in successful and failure scenarios. The careful and appropriate application of the proposed approach in this work, with improvement and customization for each domain, can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. The code used in this work is publicly available at: www.github.com/mas-takayama/LLM-and-SCD