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.

翻译：在众多学科领域中，研究者常利用机器学习算法识别最可能从治疗中获益（"特殊应答者"）或受其伤害的个体亚组。针对这一亚组识别问题，常见方法包含两个步骤：首先，研究者使用机器学习算法估计条件平均处理效应（CATE）；其次，基于估计的CATE选择预测受治疗影响最大（正向或负向）的个体。然而，CATE估计通常存在偏倚和噪声，且使用同一数据同时识别亚组并估计其群体平均处理效应会引发多重检验问题。为解决这些挑战，我们开发了基于通用机器学习算法排序的群体平均处理效应估计的统一置信带（GATES）。借助这些统一置信带，研究者可在统计保障下识别出GATES超过特定效应量的亚组，无论该效应量如何选择。该方法的有效性仅依赖于治疗随机化与单位随机抽样。重要的是，我们的方法无需建模假设且避免了计算密集型的重抽样过程。模拟研究表明，即使样本量小至100，所提出的统一置信带仍具有合理的信息量和适当的经验覆盖度。我们分析了晚期前列腺癌的临床试验数据，发现其中存在比例较高的特殊应答者。