AI 中文总结
研究个性化干预效益，引入统计假设检验，根据历史数据评估个性化干预政策性能能否超越最佳单一干预，在特定条件下控制错误率并实现渐近正态性和最小方差，不同数据集结果证明其通用性及优势，可助力干预科学决策。
AI 中文摘要
从医学到市场营销再到社会科学，为个体量身定制干预措施的前景不可否认。然而，实际应用中需权衡个性化潜在益处与其可能增加的成本和脆弱性。我们引入一种统计假设检验，根据历史数据评估个性化干预政策的性能是否会超过采用最佳单一干预措施的证据。该检验在特定条件下保持严格的第一类错误控制，同时实现渐近正态性和最小方差。来自就业培训、抑郁症治疗、教育和推荐系统等不同数据集的结果证明了该检验的通用性及其优于其他方法的性能。此检验可为干预科学领域的决策者提供支持，通过简单有力地量化个性化的潜在益处。
英文摘要
From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's potential benefits with its possible increased cost and fragility. We introduce a statistical hypothesis test that evaluates, given historical data, evidence that a personalized intervention policy's performance will surpass deploying the best single intervention. The test maintains strict type-I error control while achieving asymptotic normality with the minimal possible variance under specified conditions. Results on diverse datasets from job training, depression treatment, education and recommendation systems demonstrate the test's versatility and its superior performance over alternatives. This test can support decision-makers throughout the intervention sciences by providing a simple and powerful quantification of the potential benefits of personalization.
Journal refScience 393, eaeb9506 (2026)
