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
this https URL

通过引入因果路径和前门调整，Causal Walk 是一种用于从因果角度消除多跳事实验证偏差的新方法，通过随机游走的思想估计处理和中介变量之间的因果效应，并利用归一化加权几何平均逼近估计中介变量和结果变量之间的因果效应。

因果步行：使用前门调整消除多跳事实验证中的偏倚

Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door  Adjustment

The opaqueness of the multi-hop fact verification model imposes imperative
requirements for explainability. One feasible way is to extract rationales, a
subset of inputs, where the performance of prediction drops dramatically when
being removed. Though being explainable, most rationale extraction methods for
multi-hop fact verification explore the semantic information within each piece
of evidence individually, while ignoring the topological information
interaction among different pieces of evidence. Intuitively, a faithful
rationale bears complementary information being able to extract other
rationales through the multi-hop reasoning process. To tackle such
disadvantages, we cast explainable multi-hop fact verification as subgraph
extraction, which can be solved based on graph convolutional network (GCN) with
salience-aware graph learning. In specific, GCN is utilized to incorporate the
topological interaction information among multiple pieces of evidence for
learning evidence representation. Meanwhile, to alleviate the influence of
noisy evidence, the salience-aware graph perturbation is induced into the
message passing of GCN. Moreover, the multi-task model with three diagnostic
properties of rationale is elaborately designed to improve the quality of an
explanation without any explicit annotations. Experimental results on the
FEVEROUS benchmark show significant gains over previous state-of-the-art
methods for both rationale extraction and fact verification.

利用图卷积网络和注意力因素，将可解释的多跳事实验证问题转化为子图提取，实现证据的拓扑交互，构建多任务模型区分警示性因素，对通过 FEVEROUS 基准测试得到的实验结果验证上述方法优于先前最先进算法的功效。