利用聚合二元数据在干扰下无偏估计总处理效应
Unbiased Estimation for Total Treatment Effect Under Interference Using Aggregated Dyadic Data
AI总结：
针对社交网络互动干扰下的因果效应估计问题，本文提出一种基于伯努利随机化设计和聚合二元数据的TTE无偏估计量，并通过数值实验证明其优于常用估计量。
摘要：
在社交媒体平台中，用户行为常常受到与其他用户互动的影响，这使得传统A/B实验中因果效应的准确估计变得复杂。本研究考察了个体结果可以分解为多个成对结果之和的情形，这反映了用户之间的互动。这些被称为二元数据（dyadic data）的结果在许多社交网络情境中普遍存在。利用伯努利随机化设计，我们提出了一种新颖的总处理效应（TTE）无偏估计量，用于量化当所有个体被分配到处理组与对照组时总体均值的差异。我们进一步探讨了在无法将所有个体纳入实验的情况下该估计量的偏差，这是在线对照实验中常见的约束。我们的数值结果表明，所提出的估计量持续优于一些常用估计量，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。
英文摘要：
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.
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