On the promise that if human users know the cause of an output, it would enable them to grasp the process responsible for the output, and hence provide understanding, many explainable methods have been proposed to indicate the cause for the output of a model based on its input. Nonetheless, little has been reported on quantitative measurements of such causal relationships between the inputs, the explanations, and the outputs of a model, leaving the assessment to the user, independent of his level of expertise in the subject. To address this situation, we explore a technique for measuring the causal relationship between the features from the area of the object of interest in the images of a class and the output of a classifier. Our experiments indicate improvement in the causal relationships measured when the area of the object of interest per class is indicated by a mask from an explainable method than when it is indicated by human annotators. Hence the chosen name of Causal Explanation Score (CaES)

翻译：在假设人类用户若能知晓输出的原因，将能够理解产生该输出的过程，从而获得理解的基础上，许多可解释方法已被提出，用于根据模型输入指示其原因。然而，关于输入、解释和模型输出之间此类因果关系的定量测量鲜有报道，这导致评估依赖于用户，而与其在该领域的专业水平无关。为应对这一情况，我们探索了一种技术，用于测量图像中每类感兴趣对象区域的特征与分类器输出之间的因果关系。我们的实验表明，当每类感兴趣对象区域由可解释方法生成的掩膜指示时，所测量的因果关系比由人类注释者指示时有所改善。因此，我们将该方法命名为因果解释分数（CaES）。