  Counterfactual explanations have become a mainstay of the XAI field. This
particularly intuitive statement allows the user to understand what small but
necessary changes would have to be made to a given situation in order to change
a model prediction. The quality of a counterfactual depends on several
criteria: realism, actionability, validity, robustness, etc. In this paper, we
are interested in the notion of robustness of a counterfactual. More precisely,
we focus on robustness to counterfactual input changes. This form of robustness
is particularly challenging as it involves a trade-off between the robustness
of the counterfactual and the proximity with the example to explain. We propose
a new framework, CROCO, that generates robust counterfactuals while managing
effectively this trade-off, and guarantees the user a minimal robustness. An
empirical evaluation on tabular datasets confirms the relevance and
effectiveness of our approach.
