Multicriteria Model-Agnostic Counterfactual Explainability for Classifiers

First, a procedure is developed for providing a multicriteria model-agnostic Counterfactual Explanation (CE) for classifiers, which is formalized as a constrained multi-objective optimization problem. The proposed multicriteria framework takes into account the proximity between different input vector values by breaking down the influence of each actionable variable. This way, the resulting CE Pareto set allows for a flexible consideration of the relative relevance of such actionable variables. Next, the procedure is extended for the case in which the classifier is not accessible and only a set of labeled data is available. Several architectures are considered to approximate the classifier and a combined post-processing of the resulting approximations of the CE Pareto set is also proposed, that is aimed to two relevant objectives: to improve the approximation of such CE Pareto set and to comparatively assess the performance of the architectures. A simulation example illustrates the theoretical results and the applicability of the proposed schemes.