The prevalence of machine learning models in various industries has led to growing
demands for model interpretability and for the ability to provide meaningful recourse to
users. For example, patients hoping to improve their diagnoses or loan applicants seeking to
increase their chances of approval. Counterfactuals can help in this regard by identifying
input perturbations that would result in more desirable prediction outcomes. Meaningful
counterfactuals should be able to achieve the desired outcome, but also be realistic …