  Given two object images, how can we explain their differences in terms of the
underlying object properties? To address this question, we propose
Align-Deform-Subtract (ADS) -- an interventional framework for explaining
object differences. By leveraging semantic alignments in image-space as
counterfactual interventions on the underlying object properties, ADS
iteratively quantifies and removes differences in object properties. The result
is a set of "disentangled" error measures which explain object differences in
terms of the underlying properties. Experiments on real and synthetic data
illustrate the efficacy of the framework.
