Ranking Counterfactual Explanations

• URL: http://arxiv.org/abs/2503.15817v1
• Date: Thu, 20 Mar 2025 03:04:05 GMT
• Title: Ranking Counterfactual Explanations
• Authors: Suryani Lim, Henri Prade, Gilles Richard,
• Abstract summary: Explanations can address two key questions: "Why this outcome?" (factual) and "Why not another?" (counterfactual)<n>This paper proposes a formal definition of counterfactual explanations, proving some properties they satisfy, and examining the relationship with factual explanations.
• Score: 7.066382982173528
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: AI-driven outcomes can be challenging for end-users to understand. Explanations can address two key questions: "Why this outcome?" (factual) and "Why not another?" (counterfactual). While substantial efforts have been made to formalize factual explanations, a precise and comprehensive study of counterfactual explanations is still lacking. This paper proposes a formal definition of counterfactual explanations, proving some properties they satisfy, and examining the relationship with factual explanations. Given that multiple counterfactual explanations generally exist for a specific case, we also introduce a rigorous method to rank these counterfactual explanations, going beyond a simple minimality condition, and to identify the optimal ones. Our experiments with 12 real-world datasets highlight that, in most cases, a single optimal counterfactual explanation emerges. We also demonstrate, via three metrics, that the selected optimal explanation exhibits higher representativeness and can explain a broader range of elements than a random minimal counterfactual. This result highlights the effectiveness of our approach in identifying more robust and comprehensive counterfactual explanations.

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