In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns. Networks data is often assumed to perfectly represent these possible interactions. This paper considers the problem of estimating treatment effects when measured connections are, instead, a noisy representation of the true spillover pathways. We show that existing methods, using the potential outcomes framework, yield biased estimators in the presence of this mismeasurement. We develop a new method, using a class of mixture models, that can account for missing connections and discuss its estimation via the Expectation-Maximization algorithm. We check our method's performance by simulating experiments on real network data from 43 villages in India. Finally, we use data from a previously published study to show that estimates using our method are more robust to the choice of network measure.

翻译：在许多实验情境中，网络互动如何影响处理组和对照组个体的关注结果是一个关键问题。网络数据通常被假定能完美表征这些可能的互动。本文考虑当测量到的连接实际上是真实溢出路径的含噪表征时，如何估计处理效应的问题。我们表明，现有基于潜在结果框架的方法在存在这种测量误差时会产生有偏估计。我们开发了一种新方法，利用混合模型类来考虑缺失连接，并讨论了通过期望最大化算法进行估计的过程。我们通过在印度43个村庄的真实网络数据上模拟实验来检验方法的性能。最后，我们使用来自先前已发表研究的数据表明，采用我们方法得到的估计对网络测量方式的选择更为稳健。