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

2026-07-01

AI Summary

This study addresses the limitations of existing counterfactual explanation methods in regression tasks, which rely on exogenous target values and distance functions lacking economic interpretability, thereby hindering profit-driven decision-making. To overcome this, the paper proposes a Profit-Based Counterfactual Explanation (PBCE) framework that directly optimizes profit as the objective and models feature modification costs as a distance term with clear economic meaning, eliminating the need for manually specified target values. By integrating predictive models with cost constraints, PBCE employs optimization algorithms to generate actionable product improvement recommendations. Empirical evaluation on a Japanese manga sales dataset demonstrates that counterfactuals produced by PBCE significantly enhance profitability while maintaining practical feasibility, outperforming conventional target-oriented approaches.

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.

Problem

• counterfactual explanation
• profit maximization
• regression
• decision-making
• product improvement

Innovation

• profit-based counterfactual explanation
• counterfactual explanation
• profit maximization
• attribute modification cost
• interpretable machine learning