SPIN Unprocessed July 3, 2026 ai_technology research
Profit-Based Counterfactual Explanations for Product Improvement: A Case Study of Manga Sales in Japan
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arXiv:2607.01610v1 Announce Type: new 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 inte
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