  Multi-hop QA requires reasoning over multiple supporting facts to answer the
question. However, the existing QA models always rely on shortcuts, e.g.,
providing the true answer by only one fact, rather than multi-hop reasoning,
which is referred as $\textit{disconnected reasoning}$ problem. To alleviate
this issue, we propose a novel counterfactual multihop QA, a causal-effect
approach that enables to reduce the disconnected reasoning. It builds upon
explicitly modeling of causality: 1) the direct causal effects of disconnected
reasoning and 2) the causal effect of true multi-hop reasoning from the total
causal effect. With the causal graph, a counterfactual inference is proposed to
disentangle the disconnected reasoning from the total causal effect, which
provides us a new perspective and technology to learn a QA model that exploits
the true multi-hop reasoning instead of shortcuts. Extensive experiments have
conducted on the benchmark HotpotQA dataset, which demonstrate that the
proposed method can achieve notable improvement on reducing disconnected
reasoning. For example, our method achieves 5.8% higher points of its Supp$_s$
score on HotpotQA through true multihop reasoning. The code is available at
supplementary material.
