  How can we understand classification decisions made by deep neural networks?
Many existing explainability methods rely solely on correlations and fail to
account for confounding, which may result in potentially misleading
explanations. To overcome this problem, we define the Causal Concept Effect
(CaCE) as the causal effect of (the presence or absence of) a
human-interpretable concept on a deep neural net's predictions. We show that
the CaCE measure can avoid errors stemming from confounding. Estimating CaCE is
difficult in situations where we cannot easily simulate the do-operator. To
mitigate this problem, we use a generative model, specifically a Variational
AutoEncoder (VAE), to measure VAE-CaCE. In an extensive experimental analysis,
we show that the VAE-CaCE is able to estimate the true concept causal effect,
compared to baselines for a number of datasets including high dimensional
images.
