边处理与节点结果的网络实验
Network Experiments with Edge Treatments and Node Outcomes
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中文总结 AI 辅助
本文提出一种在无向图上进行边级处理实验、分析节点结果的方法，利用节点分配状态作为工具变量，证明Wald估计量在边权重误设时仍一致且渐近正态，并模拟验证其有效性。
中文摘要 AI 辅助
我们提出了一种方法论，用于在由无向图连接的总体中，对边级处理进行实验的同时分析节点级结果。在我们的设计下，节点被随机分配到测试组或对照组，每条边继承其端点的处理状态，冲突通过随机化解决。我们使用每个节点的分配状态作为其处理暴露的工具。我们证明，即使用于构建暴露的边权重被错误设定，Wald估计量对全局平均处理效应（GATE）仍是一致的。我们形式化了所需的假设，既涉及潜在结果的线性性，也涉及图的稀疏性，并证明了Wald估计量的渐近正态性。该估计量易于实现，无需在图上进行分配模拟。我们的蒙特卡洛研究展示了该方法在社交平台情境下的强劲表现。
英文摘要
We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment effect (GATE), even when the edge weights used to construct the exposure are misspecified. We formalize the assumptions needed both in terms of the linearity of potential outcomes and the sparsity of the graph, and prove the asymptotic normality of the Wald estimator. The estimator is straightforward to implement, requiring no assignment simulations over the graph. Our Monte Carlo study demonstrates the strong performance of our approach in the context of a social platform.
发表机构
- Meta
- Ads Online Experimentation, Meta（Meta在线广告实验部门）
机构由 AI 辅助整理，请以论文原文为准。
