  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.
