Instance-wise Causal Feature Selection for Model Interpretation
This provides a causal interpretation method for visual classifiers, though it appears incremental as an extension of existing instance-wise feature selection paradigms.
The paper tackles the problem of explaining black-box visual classifiers by developing a causal extension to instance-wise feature selection that identifies input features with the greatest causal effect on model outputs, resulting in sparser selections that cover salient objects in scenes.
We formulate a causal extension to the recently introduced paradigm of instance-wise feature selection to explain black-box visual classifiers. Our method selects a subset of input features that has the greatest causal effect on the models output. We quantify the causal influence of a subset of features by the Relative Entropy Distance measure. Under certain assumptions this is equivalent to the conditional mutual information between the selected subset and the output variable. The resulting causal selections are sparser and cover salient objects in the scene. We show the efficacy of our approach on multiple vision datasets by measuring the post-hoc accuracy and Average Causal Effect of selected features on the models output.