Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target label of a given input based on its high-level concepts. Unlike other end-to-end deep learning models, CBMs enable domain experts to intervene on the predicted concepts at test time so that more accurate and reliable target predictions can be made. While the intervenability provides a powerful avenue of control, many aspects of the intervention procedure remain underexplored. In this work,…