Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://github.com/zcccccz/CausalWalk.

翻译：传统多跳事实验证模型易受标注伪影中的虚假相关性影响，导致在无偏数据集上性能显著下降。在众多去偏研究中，基于因果推断的方法通过实施理论保障的去偏（如因果干预或反事实推理）而备受关注。然而，现有基于因果推断的去偏方法主要将事实验证建模为单跳推理任务以处理浅层偏差模式，无法应对多跳证据中隐藏的复杂偏差模式。为应对这一挑战，我们提出Causal Walk方法，这是一种从因果视角出发、通过前门调整实现多跳事实验证去偏的新方法。具体而言，在结构因果模型中，引入处理变量（输入声明-证据图）与结果变量（真实性标签）之间的推理路径作为中介变量以阻断混淆因子。通过前门调整，处理变量与结果变量之间的因果效应被分解为：处理变量与中介变量之间的因果效应（利用随机游走思想估计）以及中介变量与结果变量之间的因果效应（通过归一化加权几何均值近似估计）。为验证所提方法的有效性，我们借助大语言模型构建了对抗性多跳事实验证数据集和对称性多跳事实验证数据集。实验结果表明，Causal Walk在现有数据集及新构建数据集上均优于部分以往去偏方法。代码与数据将在https://github.com/zcccccz/CausalWalk 公开。