Profit-Based Counterfactual Explanations Optimize Product Improvement

Counterfactual explanations (CEs) are widely used to enhance the interpretability of machine learning models and support data-driven decision-making. However, existing CE methods often require users to specify a desired output (target) and a distance function to quantify changes in variables. This research identifies limitations in the validity of target specification and the practical interpretation of distance metrics, especially in regression settings. Furthermore, most CEs focus on altering predictions rather than optimizing explicit business objectives. To address these shortcomings, the paper proposes Profit-Based Counterfactual Explanation (PBCE, a novel framework that reformulates CE as a profit maximization problem, particularly relevant in management and marketing contexts. PBCE eliminates the need for an exogenously specified target by directly maximizing profit as its primary optimization objective. Crucially, the "distance" term in PBCE is reinterpreted as the cost associated with modifying product attributes. This provides a clear, economically grounded interpretation for decision-makers. A case study involving manga sales in Japan demonstrates how PBCE can be applied to identify product improvements that directly lead to increased profitability, offering a more actionable approach to data-driven decision-making.