Online A/B tests have become increasingly popular and important for social platforms. However, accurately estimating the global average treatment effect (GATE) has proven to be challenging due to network interference, which violates the Stable Unit Treatment Value Assumption (SUTVA) and poses a great challenge to experimental design. Existing network experimental design research was mostly based on the unbiased Horvitz-Thompson (HT) estimator with substantial data trimming to ensure unbiasedness at the price of high resultant estimation variance. In this paper, we strive to balance the bias and variance in designing randomized network experiments. Under a potential outcome model with 1-hop interference, we derive the bias and variance of the standard HT estimator and reveal their relation to the network topological structure and the covariance of the treatment assignment vector. We then propose to formulate the experimental design problem to optimize the covariance matrix of the treatment assignment vector to achieve the bias and variance balance by minimizing a well-crafted upper bound of the mean squared error (MSE) of the estimator, which allows us to decouple the unknown interference effect component and the experimental design component. An efficient projected gradient descent algorithm is presented to implement the desired randomization scheme. Finally, we carry out extensive simulation studies 2 to demonstrate the advantages of our proposed method over other existing methods in many settings, with different levels of model misspecification.

翻译：在线A/B测试已日益成为社交平台广泛应用且重要的工具。然而，由于网络干扰现象的存在，准确估计全局平均处理效应（GATE）面临显著挑战：该现象违反了稳定单位处理值假设（SUTVA），对实验设计构成重大障碍。现有网络实验设计研究多基于无偏的Horvitz-Thompson（HT）估计量，通过大量数据截断保证无偏性，但导致估计方差显著增大。本文致力于在随机化网络实验设计中实现偏差与方差的权衡。基于一阶邻域干扰的潜在结果模型，我们推导了标准HT估计量的偏差与方差表达式，揭示了其与网络拓扑结构及处理分配向量协方差的内在关联。据此，我们提出通过优化处理分配向量的协方差矩阵来构建实验设计问题：通过最小化精心构造的均方误差（MSE）上界实现偏差-方差平衡，从而将未知的干扰效应分量与实验设计分量解耦。我们提出了一种高效的投影梯度下降算法来实现所需的随机化方案。最后，通过涵盖不同程度模型误设的广泛模拟研究，验证了所提方法在多场景下相较于现有方法的优越性。