AI RESEARCH
DISCOVER: A Solver for Distributional Counterfactual Explanations
arXiv CS.LG
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ArXi:2603.16436v1 Announce Type: new Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions. Most existing methods operate at the instance level. Distributional Counterfactual Explanations (DCE) extend this setting by optimizing an optimal transport objective that balances proximity to a factual input distribution and alignment to a target output distribution, with statistical certification via chance constrained bounds.