Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
Junqi Jiang, Jianglin Lan, Francesco Leofante, Antonio Rago, Francesca Toni
TL;DR
The paper tackles generating counterfactual explanations that remain valid under bounded model parameter changes while staying plausible and proximal. It introduces PROPLACE, a provably robust and plausible CE framework that leverages interval abstractions and a bi-level MILP optimization to enforce robustness against all shifts in a bounded set $\boldsymbol{\triangle}$ and realism via a plausible region $\boldsymbol{X}_{plaus}$. It proves soundness, completeness, and convergence of the method and demonstrates state-of-the-art performance on four benchmark datasets against multiple baselines, using metrics such as $v_r$, $v_{\Delta}$, and LOF. The approach supports practical deployment by certifying robustness of explanations during model updates and offers a foundation for further enhancements in actionability, diversity, and user-centric evaluation.
Abstract
Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defined as data points with minimum distance to the input that are classified with a different label than the output. To tackle the established problem that CEs are easily invalidated when model parameters are updated (e.g. retrained), studies have proposed ways to certify the robustness of CEs under model parameter changes bounded by a norm ball. However, existing methods targeting this form of robustness are not sound or complete, and they may generate implausible CEs, i.e., outliers wrt the training dataset. In fact, no existing method simultaneously optimises for closeness and plausibility while preserving robustness guarantees. In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature. We formulate an iterative algorithm to compute provably robust CEs and prove its convergence, soundness and completeness. Through a comparative experiment involving six baselines, five of which target robustness, we show that PROPLACE achieves state-of-the-art performances against metrics on three evaluation aspects.