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We formulate a causal extension to the recently introduced paradigm ofinstance-wise feature selection to explain black-box visual classifiers. Ourmethod selects a subset of input features that has the greatest causal effecton the models output. We quantify the causal influence of a subset of featuresby the Relative Entropy Distance measure. Under certain assumptions this isequivalent to the conditional mutual information between the selected subsetand the output variable. The resulting causal selections are sparser and coversalient objects in the scene. We show the efficacy of our approach on multiplevision datasets by measuring the post-hoc accuracy and Average Causal Effect ofselected features on the models output.

Instance-wise Causal Feature Selection for Model Interpretation