Detecting anomalies in tabular data is critical for many real-world applications, such as credit card fraud detection. With the rapid advancements in large language models (LLMs), state-of-the-art performance in tabular anomaly detection has been achieved by converting tabular data into text and fine-tuning LLMs. However, these methods randomly order columns during conversion, without considering the causal relationships between them, which is crucial for accurately detecting anomalies. In this paper, we present CausalTaD, a method that injects causal knowledge into LLMs for tabular anomaly detection. We first identify the causal relationships between columns and reorder them to align with these causal relationships. This reordering can be modeled as a linear ordering problem. Since each column contributes differently to the causal relationships, we further propose a reweighting strategy to assign different weights to different columns to enhance this effect. Experiments across more than 30 datasets demonstrate that our method consistently outperforms the current state-of-the-art methods. The code for CausalTAD is available at https://github.com/350234/CausalTAD.

翻译：表格数据中的异常检测对于许多现实应用至关重要，例如信用卡欺诈检测。随着大型语言模型（LLMs）的快速发展，通过将表格数据转换为文本并微调LLMs，已在表格异常检测中实现了最先进的性能。然而，这些方法在转换过程中随机排列列的顺序，没有考虑列之间的因果关系，而这对于准确检测异常至关重要。在本文中，我们提出了CausalTaD，一种将因果知识注入LLMs用于表格异常检测的方法。我们首先识别列之间的因果关系，并根据这些因果关系对列进行重新排序。这种重新排序可以建模为一个线性排序问题。由于每一列对因果关系的贡献不同，我们进一步提出了一种重新加权策略，为不同的列分配不同的权重以增强此效果。在超过30个数据集上的实验表明，我们的方法始终优于当前最先进的方法。CausalTAD的代码可在 https://github.com/350234/CausalTAD 获取。