  Information Extraction (IE) aims to extract structural information from
unstructured texts. In practice, long-tailed distributions caused by the
selection bias of a dataset, may lead to incorrect correlations, also known as
spurious correlations, between entities and labels in the conventional
likelihood models. This motivates us to propose counterfactual IE (CFIE), a
novel framework that aims to uncover the main causalities behind data in the
view of causal inference. Specifically, 1) we first introduce a unified
structural causal model (SCM) for various IE tasks, describing the
relationships among variables; 2) with our SCM, we then generate
counterfactuals based on an explicit language structure to better calculate the
direct causal effect during the inference stage; 3) we further propose a novel
debiasing approach to yield more robust predictions. Experiments on three IE
tasks across five public datasets show the effectiveness of our CFIE model in
mitigating the spurious correlation issues.
