Counterfactual Shapley Additive Explanations
This work addresses the challenge of making model explanations actionable for model consumers, though it appears incremental as it builds on existing SHAP methods.
The authors tackled the problem of linking feature attributions to actionable recourse by proposing Counterfactual SHAP (CF-SHAP), which incorporates counterfactual information into SHAP to improve explanations, and demonstrated its superiority over existing methods on public datasets using a new metric called counterfactual-ability.
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.