📊 Results: Across 6 datasets, BetaRCE consistently achieved target robustness levels while preserving explanation quality and maintaining a competitive robustness-cost trade-off. 6/7🧵

You control both confidence level (α) and robustness threshold (δ), giving statistical guarantees that your explanation will survive changes! For formal proofs on optimal SAM sampling methods and the full theoretical foundation, check out our paper! 5/7🧵

✅ Our solution: BetaRCE - offers probabilistic guarantees for robustness to model change. It works with ANY model class, is post-hoc, and can enhance your current counterfactual methods. Plus, it allows you to control the robustness-cost trade-off. 3/7🧵

📣 New paper at #KDD2025 on robust counterfactual explanations!

Imagine an AI tells you "Increase income by $200 to get a loan". You do it, but when you reapply, the model has been updated and rejects you anyway. We solve this issue by making CFEs robust to model changes! 1/7🧵