Egocentric-Network Randomized Trials (ENRTs) are increasingly used to estimate causal effects under interference when measuring complete sociocentric network data is infeasible. ENRTs rely on egocentric network sampling, where a set of egos is first sampled, and each ego recruits a subset of its neighbors as alters. Treatments are then randomized across egos. While the observed ego-networks are disjoint by design, the underlying population network may contain edges connecting them, leading to contamination. Under a design-based framework, we show that the Horvitz-Thompson estimators of direct and indirect effects are biased whenever contamination is present. To address this, we derive bias-corrected estimators and propose a novel sensitivity analysis framework based on sensitivity parameters representing the probability or expected number of missing edges. This framework is implemented via both grid sensitivity analysis and probabilistic bias analysis, providing researchers with a flexible tool to assess the robustness of the causal estimators to contamination. We apply our methodology to the HIV Prevention Trials Network 037 study, finding that ignoring contamination may lead to underestimation of indirect effects and overestimation of direct effects.

翻译：自我中心网络随机化试验（ENRTs）在无法测量完整社会中心网络数据的情况下，越来越多地被用于估计干扰条件下的因果效应。ENRTs依赖于自我中心网络抽样方法，即首先抽样一组自我节点，每个自我节点再招募其部分邻居作为关联节点。随后对自我节点进行随机化处理。虽然观测到的自我网络在设计上是互不相交的，但底层群体网络可能包含连接这些网络的边，从而导致污染效应。在设计基的框架下，我们证明当存在污染时，直接效应和间接效应的霍维茨-汤普森估计量均会产生偏差。为解决此问题，我们推导了偏差校正估计量，并提出了一种基于敏感性参数（代表缺失边的概率或期望数量）的新型敏感性分析框架。该框架通过网格敏感性分析和概率偏差分析两种方式实现，为研究者提供了评估因果估计量对污染效应稳健性的灵活工具。我们将该方法应用于HIV预防试验网络037研究，发现忽略污染效应可能导致间接效应的低估和直接效应的高估。