With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are trained in one setting but meant to be deployed in several others. One recently identified limitation is so-called shortcut learning, whereby a model learns to associate features with the prediction target that do not maintain their relationship across settings. Famously, the watermark on chest x-rays has been demonstrated to be an instance of a shortcut feature. In this viewpoint, we attempt to give a structural characterization of shortcut features in terms of causal DAGs. This is the first attempt at defining shortcut features in terms of their causal relationship with a model’s prediction target.

随着机器学习在医疗保健应用中的使用不断增加，从业者越来越面临着预测模型的局限性，这些模型在一种环境中进行训练，但需要部署在其他几种环境中。最近发现的一个限制是所谓的快捷学习，即模型学习将特征与预测目标关联起来，而这些特征不保持跨设置的关系。众所周知，胸部 X 光片上的水印已被证明是快捷功能的一个实例。从这个角度来看，我们尝试根据因果 DAG 给出快捷特征的结构表征。这是根据快捷特征与模型预测目标的因果关系来定义快捷特征的首次尝试。