部分干扰下的高阶溢出效应
Higher-order Spillover Effects Under Partial Interference
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中文总结 AI 辅助
研究网络中单元交互时的溢出效应，针对现有邻域干扰假设过严问题，基于广义干扰假设定义新因果估计量，推导错误假设偏差，开发新估计器并经模拟和实际试验验证其性能。
中文摘要 AI 辅助
干扰是指一个单元的结果通过网络连接受到其他单元处理的影响，在网络中单元交互时经常出现。当测量交互网络时，研究人员通常对一阶邻居的溢出效应感兴趣，而当前流行方法常涉及邻域干扰假设，该假设往往过于严格。本文依赖广义干扰假设，其允许一个人的潜在结果受网络更广泛区域单元处理的影响，即“干扰集”。例如，它可以是通过社区检测算法检测到的社区，或可通过有限网络路径到达的单元集。在此假设下，我们定义了新的因果估计量来量化一阶邻居以及一般特定网络距离h处单元的溢出效应。我们采用两个具有不同概率的假设伯努利分布，分别用于h阶邻域和干扰集中的其他单元。我们首先推导了依赖错误干扰集或不正确暴露映射函数的方法的偏差。然后在广义干扰假设下开发了新的霍维茨 - 汤普森和哈杰克估计器以及相应的加权回归估计器。我们进行了一系列模拟，以评估依赖严格干扰假设和暴露映射函数的OLS估计器的偏差，以及我们的估计器在不同干扰场景和随机图中的性能。最后，我们将估计器应用于在洪都拉斯实施的一项评估母婴健康干预措施的两阶段随机试验中。
英文摘要
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.
