  Deep neural networks are complex and opaque. As they enter application in a
variety of important and safety critical domains, users seek methods to explain
their output predictions. We develop an approach to explaining deep neural
networks by constructing causal models on salient concepts contained in a CNN.
We develop methods to extract salient concepts throughout a target network by
using autoencoders trained to extract human-understandable representations of
network activations. We then build a bayesian causal model using these
extracted concepts as variables in order to explain image classification.
Finally, we use this causal model to identify and visualize features with
significant causal influence on final classification.
