CEL: Comprehensive Counterfactual Explanations Library and Benchmark

Counterfactual explanations help people understand AI decisions by showing what would need to change to get a different outcome. For example, if an AI denies a loan application, a counterfactual explanation might show that increasing income by $5,000 would result in approval—providing actionable guidance rather than just a yes/no answer.

There's been no standard way to compare different counterfactual explanation methods until now. Previous studies used different datasets, models, and evaluation approaches, making it impossible to objectively determine which methods work best. This new library (CEL) provides a unified benchmark with 18 datasets and 14 different methods tested under identical conditions.

Good counterfactual explanations need multiple qualities beyond just being minimal. The research identifies five key properties: validity (the explanation actually changes the prediction), coverage (it works across different cases), sparsity (few changes needed), proximity (changes are realistic), and plausibility (the suggested changes are actually achievable in the real world). Methods are now evaluated on all these dimensions rather than just one.

Realistic counterfactuals matter for real-world trust and fairness. The benchmark specifically tests whether suggested changes are plausible—for instance, suggesting someone lose 50 pounds overnight isn't actionable, even if it would change a health insurance decision. This focus on realism helps ensure AI explanations don't mislead people about what's actually possible.