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 direct as well as the 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：评估嵌入社会网络中的受访者受政策干预的影响，通常具有挑战性，因为网络内部以及整个网络中处理组与非处理组之间均存在网络干扰。本文提出一种建模策略，将现有随机行动者导向模型（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，我们能阻断处理效应与网络影响的任何溢出，从而在短期内将政策的直接效应与间接的网络诱导效应区分开来。因此，在总体存在网络效应的条件下，该方法能够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显著的直接效应与净效应，以及由政策关联同质性驱动的显著网络效应。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，有助于研究者与政策制定者更好地规划、设计并预测网络化社会中的政策回应。