发表机构
University of New South Wales; Florida Atlantic University（新南威尔士大学; 佛罗里达大西洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对分布偏移下异质性处理效应的实验设计问题，提出TWNA方法，通过两阶段分层设计平衡部署重要性与统计难度，在兼具部署重要性与测量难度的组上收益最大。
AI 中文摘要
随机实验常针对某一总体开展，以指导另一总体的决策。按实验比例分配会将预算浪费在部署中极少出现的组上，而按部署比例分配则会对难以精确测量的组进行欠采样。我们提出TWNA（Target-Weighted Neyman Allocation，目标加权奈曼分配），这是一种两阶段分层设计，利用组-臂结果方差的预实验估计值，为目标加权组平均处理效应（GATE）的精度分配最终阶段的样本量和处理概率。该最优规则具有闭式解，可平衡部署重要性与统计难度；当预实验方差估计值稳定时，插件规则可恢复该最优规则。我们还将TWNA扩展以处理部署构成的不确定性，无论目标组合大致已知还是完全未知，均保持稳健性。最后，我们将该权重稳健性与针对偏态、稀有事件或受污染结果的预实验稳健变体进行区分。模拟实验与真实协变量基准测试表明，当组兼具部署重要性与测量难度时，TWNA的收益最大。
英文摘要
Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups that rarely appear in deployment, whereas allocating by deployment proportions under-samples groups that are hard to measure precisely. We propose \textbf{TWNA} (Target-Weighted Neyman Allocation), a two-stage stratified design that uses pilot estimates of group--arm outcome variances to allocate final-stage sample sizes and treatment probabilities for target-weighted group average treatment effect (GATE) precision. The oracle rule has a closed form and balances deployment importance with statistical difficulty; the plug-in rule recovers it as pilot variance estimates stabilize. We also extend TWNA to handle uncertainty about deployment composition, remaining robust whether the target mix is roughly known or entirely unknown. Finally, we distinguish this weight robustness from a pilot-robust variant for skewed, rare-event, or contaminated outcomes. Simulations and real-covariate benchmarks show the largest gains when groups are both deployment-important and difficult to measure.
