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Found 2 papers, 1 papers with code
One-for-many Counterfactual Explanations by Column Generation
In this paper, we consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule, where one explanation is allocated to a subgroup of the instances.
Generating collective counterfactual explanations in score-based classification via mathematical optimization
By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints.
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