A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
Counterfactual explanations (CFEs) are essential for interpreting black-box models, yet they often become invalid when models are slightly changed. Existing methods for generating robust CFEs are often limited to specific types of models, require costly tuning, or inflexible robustness controls. We propose a novel approach that jointly models the data distribution and the space of plausible model decisions to ensure robustness to model changes. Using a probabilistic consensus over a model ensemble, we train a conditional normalizing flow that captures the data density under varying levels of classifier agreement. At inference time, a single interpretable parameter controls the robustness ...
| Authors | Marcin Kostrzewa, Maciej Zięba, Jerzy Stefanowski |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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