发表机构
University of Chicago（芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出在干扰下利用再随机化设计，通过限制协变量不平衡来改进Hájek估计器对期望平均处理效应的估计，在温和条件下渐近提升精度，并开发了保守方差估计器，保持无损害性质且对干扰结构不可知。
AI 中文摘要
协变量在随机化实验中广泛用于提高精度。然而，在存在干扰的情况下，即结果可能依赖于其他单元的处理分配时，标准的协变量调整方法可能无法保持理想的性质，如无损害性质，即纳入协变量不会使估计器性能变差。现有的在干扰下纳入协变量的方法通常依赖于指定特定的干扰结构，其保证可能对误设敏感。在本文中，我们研究如何以保持无损害性质且对潜在干扰结构保持基本不可知的方式纳入协变量信息。我们聚焦于使用Hájek估计器估计期望平均处理效应（EATE），并研究干扰下的再随机化，这是一种设计阶段程序，将分配空间限制为协变量不平衡足够小的分配。我们的框架还允许用于再随机化的协变量依赖于处理分配本身，例如处理邻居的比例，这在干扰环境中自然出现。我们证明，即使在干扰下，相对于无限制的伯努利随机化，再随机化可以渐近地提高估计精度，仅依赖于跨单元依赖结构的温和条件。当存在保守的依赖图时，我们进一步开发了一种基于优化的保守方差估计器，用于再随机化下的推断。
英文摘要
Covariates are widely used in randomized experiments to improve precision. However, in the presence of interference, where outcomes may depend on the treatment assignments of other units, standard covariate adjustment methods may fail to preserve desirable properties such as the no-harm property, meaning that incorporating covariates does not worsen estimator performance. Existing approaches that incorporate covariates under interference typically rely on specifying a particular interference structure, and their guarantees can be sensitive to misspecification. In this paper, we study how to incorporate covariate information in a way that preserves the no-harm property while remaining largely agnostic to the underlying interference structure. We focus on the estimation of the expected average treatment effect (EATE) using the Hájek estimator and study rerandomization under interference, a design-stage procedure that restricts the assignment space to allocations with sufficiently small covariate imbalance. Our framework also allows the covariates used for rerandomization to depend on the treatment assignment itself, such as the proportion of treated neighbors, which naturally arises in settings with interference. We show that, even under interference, rerandomization can improve estimation precision asymptotically relative to unrestricted Bernoulli randomization, relying only on mild conditions on the dependence structure across units. When a conservative dependence graph is available, we further develop an optimization-based conservative variance estimator for inference under rerandomization.
