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。

我们漏掉了谁？对被低估人口的原则性描述方法

Who Are We Missing? A Principled Approach to Characterizing the  Underrepresented Population

The need to evaluate treatment effectiveness is ubiquitous in most of
empirical science, and interest in flexibly investigating effect heterogeneity
is growing rapidly. To do so, a multitude of model-agnostic, nonparametric
meta-learners have been proposed in recent years. Such learners decompose the
treatment effect estimation problem into separate sub-problems, each solvable
using standard supervised learning methods. Choosing between different
meta-learners in a data-driven manner is difficult, as it requires access to
counterfactual information. Therefore, with the ultimate goal of building
better understanding of the conditions under which some learners can be
expected to perform better than others a priori, we theoretically analyze four
broad meta-learning strategies which rely on plug-in estimation and
pseudo-outcome regression. We highlight how this theoretical reasoning can be
used to guide principled algorithm design and translate our analyses into
practice by considering a variety of neural network architectures as
base-learners for the discussed meta-learning strategies. In a simulation
study, we showcase the relative strengths of the learners under different
data-generating processes.

本研究论述了一些治疗效果评估的方法，探究了不同的元学习策略并以神经网络为基础，旨在构建更好的条件 / 环境下，使某些学习者的表现优于其他学习者的认识。