Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose a modeling strategy that combines existing work on stochastic actor-oriented models (SAOM) with a novel network sampling method based on the identification of independent sets. By assigning respondents from an independent set to the treatment, we are able to block any spillover of the treatment and network influence, thereby allowing us to isolate the direct effect of the treatment from the indirect network-induced effects, in the immediate term. As a result, our method allows for the estimation of both the \textit{direct} as well as the \textit{net effect} of a chosen policy intervention, in the presence of network effects in the population. We perform a comparative simulation analysis to show that our proposed sampling technique leads to distinct direct and net effects of the policy, as well as significant network effects driven by policy-linked homophily. This study highlights the importance of network sampling techniques in improving policy evaluation studies and has the potential to help researchers and policymakers with better planning, designing, and anticipating policy responses in a networked society.

翻译：评估嵌入社交网络的受访者受政策干预的影响，常因处理组内以及整个网络中处理组与非处理组间的网络干扰而颇具挑战。本文提出一种建模策略，将已有的随机行为者导向模型（SAOM）与基于独立集识别的新型网络抽样方法相结合。通过将独立集中的受访者分配至处理组，我们能够阻断处理效应与网络影响的任何溢出，从而在短期内将处理的直接效应与网络引发的间接效应分离开来。因此，本方法能够在总体中存在网络效应的情境下，估计所选政策干预的"直接效应"与"净效应"。我们通过比较模拟分析证明，所提出的抽样技术能产生政策的不同直接效应与净效应，以及由政策驱动的同质性引发的显著网络效应。本研究凸显了网络抽样技术在改进政策评估研究中的重要性，有助于研究者和政策制定者在网络化社会中更好地规划、设计和预测政策响应。