  Existing algorithms for explaining the output of image classifiers perform
poorly on inputs where the object of interest is partially occluded. We present
a novel, black-box algorithm for computing explanations that uses a principled
approach based on causal theory. We implement the method in the tool CET
(Compositional Explanation Tool). Owing to the compositionality in its
algorithm, CET computes explanations that are much more accurate than those
generated by the existing explanation tools on images with occlusions and
delivers a level of performance comparable to the state of the art when
explaining images without occlusions.
