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：自我中心网络随机试验（ENRTs）正越来越多地被用于在无法测量完整社会中心网络数据时，估计存在干扰条件下的因果效应。ENRTs依赖于自我中心网络抽样：首先抽取一组核心个体（ego），每个核心个体招募其部分邻居作为附属个体（alter），随后在核心个体间随机分配处理。尽管观测到的核心个体网络在设计上不重叠，但潜在总体网络中可能存在连接不同核心个体网络的边，从而导致污染。在基于设计的框架下，我们证明无论何时存在污染，直接效应和间接效应的霍维茨-汤普森估计量都会产生偏差。为应对这一问题，我们推导了偏差校正估计量，并提出了一个基于敏感性参数（表示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鲁棒性的灵活工具。我们将该方法应用于HIV预防试验网络037研究，发现忽略污染可能导致低估间接效应并高估直接效应。