Randomized controlled trials (RCTs) serve as the cornerstone for
understanding causal effects, yet extending inferences to target populations
presents challenges due to effect heterogeneity and underrepresentation. Our
paper addresses the critical issue of identifying and characterizing
underrepresented subgroups in RCTs, proposing a novel framework for refining
target populations to improve generalizability. We introduce an
optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to
characterize underrepresented groups. ROOT optimizes the target subpopulation
distribution by minimizing the variance of the target average treatment effect
estimate, ensuring more precise treatment effect estimations. Notably, ROOT
generates interpretable characteristics of the underrepresented population,
aiding researchers in effective communication. Our approach demonstrates
improved precision and interpretability compared to alternatives, as
illustrated with synthetic data experiments. We apply our methodology to extend
inferences from the Starting Treatment with Agonist Replacement Therapies
(START) trial -- investigating the effectiveness of medication for opioid use
disorder -- to the real-world population represented by the Treatment Episode
Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our
framework offers a systematic approach to enhance decision-making accuracy and
inform future trials in diverse populations.

通过最小化目标平均治疗效果估计的方差，我们提出了一种新的优化方法，称为优化树的拉莎蒙集（ROOT），以识别和描述随机对比试验（RCTs）中的少数群体，从而改善推广性。与其他方法相比，ROOT 具有更高的精确性和可解释性，通过合成数据实验证明了这一点。我们将该方法应用于从 Starting Treatment with Agonist Replacement Therapies（START）试验中推广到以 TEDS-A 代表的真实世界人口的决策准确性的提升和未来多样化人群中的试验的指导。