Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan

• URL: http://arxiv.org/abs/2607.01610v1
• Date: Thu, 02 Jul 2026 02:28:35 GMT
• Title: Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
• Abstract summary: Counterfactual explanation (actual explanation) is widely used to enhance the interpretability of machine learning models.<n>Existing counterfactual explanation methods typically require two quantly specified inputs: a desired output value (target) and a distance function thatifies changes in explanatory variables.<n>We propose a profit-based counterfactual explanation (PBCE) to address these limitations.
• Score: 0.6875312133832078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterfactual explanation (CE) is widely used to enhance the interpretability of machine learning models and support data-driven decision-making based on model predictions. However, existing CE methods typically require two exogenously specified inputs: a desired output value (target) and a distance function that quantifies changes in explanatory variables. In regression settings, neither the validity of target specification nor the practical interpretation of the distance metric has been sufficiently addressed. Furthermore, most existing CE methods focus on altering predictions rather than optimizing a decision objective, even though real-world decision-making often requires explicit objective maximization. To address these limitations, we formulate CE as a profit maximization problem in management and marketing contexts and propose a framework termed profit-based counterfactual explanation (PBCE). PBCE eliminates the need for exogenous target specification by directly maximizing profit as the primary optimization objective. Concurrently, the distance term is reinterpreted as the cost of modifying product attributes, providing a clear and economically grounded interpretation.

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