In social media platforms, user behavior is often influenced by interactions with other users, complicating the accurate estimation of causal effects in traditional A/B experiments. This study investigates situations where an individual's outcome can be broken down into the sum of multiple pairwise outcomes, a reflection of user interactions. These outcomes, referred to as dyadic data, are prevalent in many social network contexts. Utilizing a Bernoulli randomized design, we introduce a novel unbiased estimator for the total treatment effect (TTE), which quantifies the difference in population mean when all individuals are assigned to treatment versus control groups. We further explore the bias of our estimator in scenarios where it is impractical to include all individuals in the experiment, a common constraint in online control experiments. Our numerical results reveal that our proposed estimator consistently outperforms some commonly used estimators, underscoring its potential for more precise causal effect estimation in social media environments.

翻译：在社交媒体平台中，用户行为常受其他用户交互的影响，这使得传统A/B实验中因果效应的准确估计变得复杂。本研究探讨了个体结果可分解为多个成对结果之和的情形，这反映了用户交互的本质。这些结果被称为二元数据，在众多社交网络环境中普遍存在。利用伯努利随机化设计，我们提出了一种针对总处理效应（TTE）的新型无偏估计量，该效应量化了所有个体均被分配到处理组与对照组时总体均值的差异。我们进一步探讨了当实验无法包含所有个体时估计量的偏差——这是在线控制实验中的常见约束。数值结果表明，我们提出的估计量始终优于某些常用估计量，凸显了其在社交媒体环境中实现更精确因果效应估计的潜力。