Social network interference induces complex dependencies where a unit's outcome is influenced not only by its own exposure and mediator but also by those of connected neighbors. In such settings, a significant challenge lies in distinguishing direct exposure effects from interference-driven spillover effects, and further separating these from indirect effects mediated by intermediate variables. To address this, we propose a theoretical framework utilizing structural graphical models. Central to our approach is the Random Effects Network Structural Equation Model (REN-SEM), which extends the exposure mapping paradigm to capture these multifaceted spillover and mediation mechanisms while accounting for latent dependencies within mediators and outcomes. We establish general identification conditions and derive decomposition formulas for six distinct mechanistic estimands. Furthermore, for the class of Linear REN-SEMs, we develop a maximum likelihood estimation framework and establish a rigorous asymptotic theory tailored to non-i.i.d. network data, proving the consistency of our estimators and the validity of the variance estimates. The robustness and practical utility of our methodology are demonstrated through simulation experiments and an analysis of the Twitch Gamers Network, underscoring its effectiveness in quantifying intricate network-mediated exposure effects.

翻译：社交网络干扰会引发复杂的依赖关系，其中个体的结果不仅受其自身暴露和中介变量的影响，还受到相连邻居的暴露和中介变量的影响。在此类情境下，一个关键挑战在于区分直接暴露效应与干扰驱动的溢出效应，并进一步将这些效应与通过中间变量传递的间接效应分离开来。为解决这一问题，我们提出了一个基于结构图模型的理论框架。我们方法的核心是随机效应网络结构方程模型（REN-SEM），该模型扩展了暴露映射范式，以捕捉这些多方面的溢出和中介机制，同时考虑中介变量与结果中潜在的依赖关系。我们建立了一般的可识别性条件，并推导了六种不同机制估计量的分解公式。此外，针对线性REN-SEM类模型，我们开发了最大似然估计框架，并建立了专门针对非独立同分布网络数据的严格渐近理论，证明了我们估计量的一致性以及方差估计的有效性。通过仿真实验和对Twitch游戏玩家网络的分析，我们展示了所提方法的稳健性和实际效用，突显了其在量化复杂网络中介的暴露效应方面的有效性。