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Being able to provide explanations for a model's decision has become acentral requirement for the development, deployment, and adoption of machinelearning models. However, we are yet to understand what explanation methods canand cannot do. How do upstream factors such as data, model prediction,hyperparameters, and random initialization influence downstream explanations?While previous work raised concerns that explanations (E) may have littlerelationship with the prediction (Y), there is a lack of conclusive study toquantify this relationship. Our work borrows tools from causal inference tosystematically assay this relationship. More specifically, we study therelationship between E and Y by measuring the treatment effect when interveningon their causal ancestors, i.e., on hyperparameters and inputs used to generatesaliency-based Es or Ys. Our results suggest that the relationships between Eand Y is far from ideal. In fact, the gap between 'ideal' case only increase inhigher-performing models -- models that are likely to be deployed. Our work isa promising first step towards providing a quantitative measure of therelationship between E and Y, which could also inform the future development ofmethods for E with a quantitative metric.

On the Relationship Between Explanation and Prediction: A Causal View