论文标题
网络干扰下的邻里自适应估计量
Neighborhood Adaptive Estimators for Causal Inference under Network Interference
论文作者
论文摘要
估计因果效应已成为大多数应用领域的组成部分。在这项工作中，我们考虑违反与网络连接的单位的经典无干扰假设的行为。对于障碍性，我们考虑了一个已知的网络，该网络描述了干扰可能如何扩散。与以前的工作不同，单位所经历的干扰的半径（和强度）是未知的，并且可能取决于不同的（局部）子网络和分配的处理。我们研究了在添加剂治疗效果下对这种环境中治疗的平均直接治疗效应的估计器。我们建立收敛和分布结果的速度。提出的估计量考虑了每种（局部）治疗分配模式的所有可能半径。与以前的工作相反，我们近似相关的网络干扰模式，从而良好地估计了干扰。为了处理功能工程，一个关键的创新是建议使用合成治疗方法使依赖性脱离。我们为干扰一般研究提供了模拟，经验例证和见解。
Estimating causal effects has become an integral part of most applied fields. In this work we consider the violation of the classical no-interference assumption with units connected by a network. For tractability, we consider a known network that describes how interference may spread. Unlike previous work the radius (and intensity) of the interference experienced by a unit is unknown and can depend on different (local) sub-networks and the assigned treatments. We study estimators for the average direct treatment effect on the treated in such a setting under additive treatment effects. We establish rates of convergence and distributional results. The proposed estimators considers all possible radii for each (local) treatment assignment pattern. In contrast to previous work, we approximate the relevant network interference patterns that lead to good estimates of the interference. To handle feature engineering, a key innovation is to propose the use of synthetic treatments to decouple the dependence. We provide simulations, an empirical illustration and insights for the general study of interference.
