CEL: Comprehensive Counterfactual Explanations Library and Benchmark
Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome. While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility. Despite this progress, fair and systematic evaluation remains challenging. Existing studies often rely on different data splits, predictive models, and evaluation metrics, which limits objective comparison across methods. To fill this gap, we introduce CEL (Counterfactual Explanations Library), a unified library and benchmark ...
| Authors | Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek et al. |
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| Primary category | cs.LG |
| Published | 24 July 2026 |
| Last updated at source | 3 August 2026 |
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