Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints

ArXi:2511.20236v3 Announce Type: replace Counterfactual explanations improve the actionable interpretability of machine learning models by identifying minimal changes required to achieve a desired outcome. However, existing methods often neglect dependencies among features, which can lead to unrealistic or impractical modifications. This limitation reduces the usefulness of counterfactual explanations in real-world decision- systems.