3a93a609b97ec0ab0ff5539eb79ef33a-Paper.pdf

We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in the sense that changing learned latent factors produces a change in the classifier output statistics.