When estimating a Global Average Treatment Effect (GATE) under network interference, units can have widely different relationships to the treatment depending on a combination of the structure of their network neighborhood, the structure of the interference mechanism, and how the treatment was distributed in their neighborhood. In this work, we introduce a sequential procedure to generate and select graph- and treatment-based covariates for GATE estimation under regression adjustment. We show that it is possible to simultaneously achieve low bias and considerably reduce variance with such a procedure. To tackle inferential complications caused by our feature generation and selection process, we introduce a way to construct confidence intervals based on a block bootstrap. We illustrate that our selection procedure and subsequent estimator can achieve good performance in terms of root mean squared error in several semi-synthetic experiments with Bernoulli designs, comparing favorably to an oracle estimator that takes advantage of regression adjustments for the known underlying interference structure. We apply our method to a real world experimental dataset with strong evidence of interference and demonstrate that it can estimate the GATE reasonably well without knowing the interference process a priori.

翻译：在估计网络干扰下的全局平均处理效应（GATE）时，单元与处理之间的关系可能因网络邻域结构、干扰机制结构以及处理在邻域中的分布方式的组合而存在显著差异。本文提出一种序贯方法，用于生成并选择基于图与处理的协变量，以进行基于回归调整的GATE估计。我们证明，该方法能够同时实现低偏差并显著降低方差。为解决由特征生成与选择过程引发的推断复杂性，我们引入了一种基于分组自助法构建置信区间的方法。通过多组采用伯努利设计的半合成实验，我们展示了所选协变量及后续估计器在均方根误差方面的优异性能，其结果优于利用已知潜在干扰结构进行回归调整的预言估计器。我们将该方法应用于一个存在强干扰证据的真实实验数据集，并证明其能够在未知干扰过程先验信息的情况下，合理估计GATE值。