AI 中文总结
针对随机对照试验中标准估计器精度不足问题，提出基于特定分析目标在RCT族中识别最优估计器的框架，用样本分割估计评估指标分布，通过亚马逊和相关数据集验证，表明最优估计器因目标而异，为估计器选择提供指导。
AI 中文摘要
随机对照试验（RCTs）是技术公司、药物研究和联邦机构进行因果推断的基本工具。标准均值差估计器能提供无偏治疗效果估计，但往往缺乏精度，尤其在治疗效果异质或结果呈现重尾分布时。虽有多种提高精度的方法，但近期研究表明没有单一估计器在所有数据集上都表现最优。我们提出一个基于特定分析目标在RCT族中识别最优估计器的原则框架。该方法用样本分割估计评估指标（如均方误差、遗憾值）在RCT族中的分布，能在保持渐近保证的同时对估计器进行系统比较。我们用亚马逊供应链优化技术试验样本和加强民主挑战数据集（25项干预）展示了此框架。结果表明最优估计器因分析目标而异：加权最小二乘法在推断目标上表现最佳，而均值差在决策背景下使遗憾值最小化。这项工作为估计器选择提供了可操作的指导，同时在不同研究应用中保持了方法的严谨性。
英文摘要
Randomized controlled trials (RCTs) are fundamental tools for causal inference across technology companies, pharmaceutical research, and federal agencies. While the standard difference-in-means estimator provides unbiased treatment effect estimates, it often lacks precision, particularly when treatment effects are heterogeneous or outcomes exhibit heavy-tailed distributions. Although numerous precision-enhancing methods exist---from covariate adjustment techniques to variance reduction strategies---recent research demonstrates that no single estimator performs optimally across all datasets. Rather than seeking the best estimator for individual RCTs, which risks compromising scientific validity through convenient selection, we propose a principled framework for identifying optimal estimators within families of RCTs based on specific analytical goals. Our approach uses sample splitting to estimate the distribution of evaluation metrics (e.g., mean squared error, regret) across RCT families, enabling systematic comparisons between estimators while maintaining asymptotic guarantees. We demonstrate this framework using a sample of Amazon's Supply Chain Optimization Technology trials and the Strengthening Democracy Challenge dataset (25 interventions). Results reveal that optimal estimators vary significantly by analytical objective: weighted least squares performs best for inference goals, while difference-in-means minimizes regret for decision-making contexts. This work provides actionable guidance for estimator selection while preserving methodological rigor across diverse research applications.
