A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density

arXiv:2404.13476v1 Announce Type: new
Abstract: The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations - via small perturbations to the input - has been notable in the research community. Although the variety of CF examples is important, the aspect of them being feasible at the same time, does not necessarily apply in their entirety. This work uses different benchmark datasets to examine through the preservation of the logical causal relations of their attributes, …

abstract arxiv causality community counterfactual cs.ai cs.lg examples exploration framework machine machine learning machine learning model research research community small sparsity them type via