Higher-order Spillover Effects Under Partial Interference
部分干扰下的高阶溢出效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究网络处理干扰下的因果溢出效应，并按网络距离定义目标量，因此对应因果推断与网络及干扰。
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部分干扰下的高阶溢出效应
- 研究问题
- 当个体结果受到更广网络区域的处理影响时，如何定义并估计特定网络距离上的溢出效应，避免过窄干扰假设造成偏差？
- 方法与思路
- 作者设定广义干扰集合，以两种伯努利分布区分指定阶邻域与其余单位，并构造加权估计量及对应回归估计量。
- 材料陈述的贡献
- 摘要称推导错误干扰集合或暴露映射造成的偏差，提出高阶溢出效应估计方法，并以模拟和洪都拉斯随机试验展示应用。
- 阅读关注点
- 应核对干扰集合如何确定、假想分配政策与实际随机化如何衔接，以及估计权重、方差推断和网络误测的处理方式。
- 阅读边界
- 这是预印本，同行评审及期刊接收未获核实。摘要未列出完整识别条件和应用效应数值，无法判断网络设定错误下的稳健程度。
原文摘要
Interference, under which a unit's outcome is affected by the treatment of other units through network connections, is often present when units interact on a network. When the network of interactions is measured, researchers are often interested in the spillover effect from first-order neighbors. When this is the case, the prevailing approach often involves the neighborhood interference assumption, which is oftentimes overly restrictive. In this paper, we instead rely on a generalized interference assumption, which allows one's potential outcomes to be influenced by the treatment of units from a wider area of the network, referred to as the "interference set". For instance, this can be a community detected through a community detection algorithm, or the set of units that can be reached through a finite network path. Under this assumption, we define new causal estimands to quantify spillover effects from first-order neighbors and, in general, from units at a specific network distance h. We employ two hypothetical Bernoulli distributions with different probabilities for the h-order neighborhood and for the rest of the units in the interference set. We first derive the bias of an approach that relies on a wrong interference set or incorrect exposure mapping function. We then develop new Horvitz-Thompson and Hajek estimators and corresponding weighted regression estimators under the generalized interference assumption. We conduct a series of simulations to assess the bias of OLS estimators -- which rely on restrictive interference assumptions and an exposure mapping function -- , and the performance of our estimators in different interference scenarios and random graphs. We then apply our estimators to a two-stage randomized trial implemented in Honduras to assess a maternal and child health intervention.