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Counterfactual explanations with the k-Nearest Neighborhood classifier and uncertain data

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  • Carrizosa, Emilio
  • De Leone, Renato
  • Magagnini, Marica

Abstract

Counterfactual Analysis is a powerful tool in Explainable Machine Learning. Given a classifier and a record, one seeks the smallest perturbation necessary to have the perturbed record, called the counterfactual explanation, classified in the desired class. When applied to real-world scenarios, Counterfactual Analysis may be affected by different sources of uncertainty, which should be taken into account to provide robust counterfactual explanations.

Suggested Citation

  • Carrizosa, Emilio & De Leone, Renato & Magagnini, Marica, 2026. "Counterfactual explanations with the k-Nearest Neighborhood classifier and uncertain data," European Journal of Operational Research, Elsevier, vol. 335(2), pages 584-596.
    • Handle: RePEc:eee:ejores:v:335:y:2026:i:2:p:584-596
    DOI: 10.1016/j.ejor.2026.06.041
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