Counterfactual Shapley Additive Explanations

TL;DR

This paper introduces CF-SHAP, a new feature attribution method that incorporates counterfactual information to improve explanations in actionable recourse settings, demonstrating its superiority over existing methods.

Contribution

It proposes CF-SHAP, a variant of SHAP that uses counterfactual data to enhance feature attribution explanations for model recourse.

Findings

CF-SHAP outperforms existing methods on public datasets.

A new quantitative score, counterfactual-ability, is introduced.

Careful background dataset selection improves attribution quality.

Abstract

Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model. In the actionable recourse setting, wherein the goal of the explanations is to improve outcomes for model consumers, it is often unclear how feature attributions should be correctly used. With this work, we aim to strengthen and clarify the link between actionable recourse and feature attributions. Concretely, we propose a variant of SHAP, Counterfactual SHAP (CF-SHAP), that incorporates counterfactual information to produce a background dataset for use within the marginal (a.k.a. interventional) Shapley value framework. We motivate the need within the actionable recourse setting for careful consideration of background datasets when using Shapley values for feature attributions with numerous synthetic examples. Moreover, we demonstrate the efficacy of CF-SHAP by proposing and justifying a quantitative score for feature attributions, counterfactual-ability, showing that as measured by this metric, CF-SHAP is superior to existing methods when evaluated on public datasets using tree ensembles.