用知识图谱子图提升大模型因果推理准确率
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
- 从知识图谱中提取关键路径子图作为推理线索
- 在生物医学和开放领域数据上提升44.4点F1分数
- 适合需要可靠因果推断的研究者使用
推断变量对之间的因果关系对于理解复杂系统中的多变量交互至关重要。基于知识的因果发现——通过推理变量元数据（如名称或文本上下文）来推断因果关系——为依赖观测数据的传统方法提供了有力替代方案。然而，现有基于大语言模型（LLMs）的方法常产生不稳定且不一致的结果，影响其在因果推断中的可靠性。为此，本文提出一种新方法，将知识图谱（KGs）与大语言模型结合，以增强基于知识的因果发现。该方法在知识图谱中识别基于元路径的有信息子图，并利用基于学习排序的模型进一步优化子图选择。将得分最高的子图融入零样本提示中，显著提升了大语言模型在因果关系推断上的效果。在生物医学和开放领域数据集上的大量实验表明，该方法在不同大语言模型和知识图谱下，最高可使F1分数提升44.4点。代码与数据已开源于GitHub：https://github.com/susantiyuni/path-to-causality
原文摘要 · Abstract (English)
Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -- which involves inferring causal relationships by reasoning over the metadata of variables (e.g., names or textual context) -- offers a compelling alternative to traditional methods that rely on observational data. However, existing methods using Large Language Models (LLMs) often produce unstable and inconsistent results, compromising their reliability for causal inference. To address this, we introduce a novel approach that integrates Knowledge Graphs (KGs) with LLMs to enhance knowledge-based causal discovery. Our approach identifies informative metapath-based subgraphs within KGs and further refines the selection of these subgraphs using Learning-to-Rank-based models. The top-ranked subgraphs are then incorporated into zero-shot prompts, improving the effectiveness of LLMs in inferring the causal relationship. Extensive experiments on biomedical and open-domain datasets demonstrate that our method outperforms most baselines by up to 44.4 points in F1 scores, evaluated across diverse LLMs and KGs. Our code and datasets are available on GitHub: https://github.com/susantiyuni/path-to-causality
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