  Model explanations based on pure observational data cannot compute the
effects of features reliably, due to their inability to estimate how each
factor alteration could affect the rest. We argue that explanations should be
based on the causal model of the data and the derived intervened causal models,
that represent the data distribution subject to interventions. With these
models, we can compute counterfactuals, new samples that will inform us how the
model reacts to feature changes on our input. We propose a novel explanation
methodology based on Causal Counterfactuals and identify the limitations of
current Image Generative Models in their application to counterfactual
creation.
