Concept-based explanation approach is a popular model interpertability tool
because it expresses the reasons for a model's predictions in terms of concepts
that are meaningful for the domain experts. In this work, we study the problem
of the concepts being correlated with confounding information in the features.
We propose a new causal prior graph for modeling the impacts of unobserved
variables and a method to remove the impact of confounding information and
noise using a two-stage regression technique borrowed from the instrumental
variable literature. We also model the completeness of the concepts set and
show that our debiasing method works when the concepts are not complete. Our
synthetic and real-world experiments demonstrate the success of our method in
removing biases and improving the ranking of the concepts in terms of their
contribution to the explanation of the predictions.

本文提出新的因果性先验图和用于消除混淆信息和噪声的两阶段回归技术，以解决模型可解释性工具中的潜在变量和概念完备性等问题，并在合成和真实数据集上进行了实验证明了该方法的成功。