  Despite the popularity of Vision Transformers (ViTs) and eXplainable AI
(XAI), only a few explanation methods have been designed specially for ViTs
thus far. They mostly use attention weights of the [CLS] token on patch
embeddings and often produce unsatisfactory saliency maps. This paper proposes
a novel method for explaining ViTs called ViT-CX. It is based on patch
embeddings, rather than attentions paid to them, and their causal impacts on
the model output. Other characteristics of ViTs such as causal
overdetermination are also considered in the design of ViT-CX. The empirical
results show that ViT-CX produces more meaningful saliency maps and does a
better job revealing all important evidence for the predictions than previous
methods. The explanation generated by ViT-CX also shows significantly better
faithfulness to the model. The codes and appendix are available at
https://github.com/vaynexie/CausalX-ViT.
