自适应加权随机隔离（AWRI）：一种在网络干扰下估计因果效应的简单设计
Adaptive Weighted Random Isolation (AWRI): a simple design to estimate causal effects under network interference
AI总结：
本文提出加权随机隔离（WRI）设计及受限均值差估计器（RDIM），结合网络自适应权重选择算法减少偏差，在有向网络中估计总处理效应，广泛模拟中优于九种现有方法。
摘要：
近年来，文献中对网络干扰下的因果推断日益关注。本文聚焦于估计总处理效应（TTE）的随机化设计，TTE定义为完全处理组与完全控制组之间潜在结果的平均差异。我们提出了一种名为加权随机隔离（WRI）的简单设计，以及用于TTE估计的受限均值差估计器（RDIM）。此外，我们为RDIM估计器推导了一种新颖的均方误差代理，并由网络自适应权重选择算法提供支持。这有助于我们为WRI设计确定一个公平的权重，从而有效减少偏差。我们的方法适用于有向网络，扩展了先前的框架。大量模拟表明，所提方法在广泛场景中优于九种现有方法。
英文摘要：
Recently, causal inference under interference has gained increasing attention in the literature. In this paper, we focus on randomized designs for estimating the total treatment effect (TTE), defined as the average difference in potential outcomes between fully treated and fully controlled groups. We propose a simple design called weighted random isolation (WRI) along with a restricted difference-in-means estimator (RDIM) for TTE estimation. Additionally, we derive a novel mean squared error surrogate for the RDIM estimator, supported by a network-adaptive weight selection algorithm. This can help us determine a fair weight for the WRI design, thereby effectively reducing the bias. Our method accommodates directed networks, extending previous frameworks. Extensive simulations demonstrate that the proposed method outperforms nine established methods across a wide range of scenarios.
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