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arXiv AI By Marcin Kostrzewa, Maciej Zi\k{e}ba

Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanations

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The paper introduces a unified evaluation protocol for robust counterfactual explanations (CFE), testing six robust methods and two baselines across four tabular datasets under eight types of model change. It shows that robustness scores vary by change type and that methods designed for one change family may not transfer to others, with RobX performing most consistently. The study emphasizes the need for a common protocol that defines model changes, measures their impact, and separates generation performance from robustness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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