Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
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通用机器学习子群识别的统计性能保证
- 研究问题
- 利用带偏差和噪声的机器学习处理效应预测筛选受益或受害子群时，如何控制数据驱动选择导致的推断风险？
- 方法与思路
- 作者为按通用机器学习预测排序的组平均处理效应构造一致置信带，据此选择效应超过阈值的子群并提供统计保证。
- 材料陈述的贡献
- 摘要称有效性仅依赖处理随机化与单位随机抽样，无需建模假设或密集重抽样，并通过模拟与前列腺癌试验展示应用。
- 阅读关注点
- 应核对排序与评估所用数据的关系、一致置信带覆盖的索引范围，以及任意效应阈值选择和子群筛选如何获得同时保证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明完整构造与随机化细节，小样本模拟覆盖表现不等于所有场景均可靠。
原文摘要
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it. A common approach to this subgroup identification problem consists of two steps. First, researchers estimate the conditional average treatment effect (CATE) using an ML algorithm. Next, they use the estimated CATE to select those individuals who are predicted to be most affected by the treatment, either positively or negatively. Unfortunately, CATE estimates are often biased and noisy. In addition, utilizing the same data to both identify a subgroup and estimate its group average treatment effect results in a multiple testing problem. To address these challenges, we develop uniform confidence bands for estimation of the group average treatment effect sorted by generic ML algorithm (GATES). Using these uniform confidence bands, researchers can identify, with a statistical guarantee, a subgroup whose GATES exceeds a certain effect size, regardless of how this effect size is chosen. The validity of the proposed methodology depends solely on randomization of treatment and random sampling of units. Importantly, our method does not require modeling assumptions and avoids a computationally intensive resampling procedure. A simulation study shows that the proposed uniform confidence bands are reasonably informative and have an appropriate empirical coverage even when the sample size is as small as 100. We analyze a clinical trial of late-stage prostate cancer and find a relatively large proportion of exceptional responders.