Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary

• 成果类型: Article; Early Access
• 署名作者: Duan, Hanyu; Yang, Yi; Abbasi, Ahmed; Tam, Kar Yan
• 署名单位: Hong Kong University of Science & Technology; University of Notre Dame; University of Notre Dame
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2022.0537
• 发表日期: 2026-03-23
• 关键词: predictive modeling; domain generalization; Machine Learning; distributionally robust optimization; uncertainty aware; analytics

摘要

Most research designing novel predictive models, or employing existing ones, assumes that training and testing data are independent and identically distributed. In practice, the data encountered at serving time often deviate from the training distribution, leading to substantial performance degradation and potential design validity and/or biased measurement issues. This challenge is further complicated by the fact that the serving time data are frequently unavailable during model development. This method commentary raises awareness of this overlooked issue through a real-world customer churn example and reviews the growing literature on domain generalization, a subfield of transfer learning that explicitly addresses situations in which the target domain is unseen during training. We further argue for adopting an uncertainty-aware predictive modeling mindset and illustrate how this perspective can be operationalized through the distributionally robust optimization framework. Finally, we offer several practical recommendations to enhance the robustness of predictive modeling under unseen data distribution shifts.