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) and diffusion contagion models 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 direct spillover of the treatment, thereby allowing us to isolate the direct effect of the treatment from the indirect network-induced effects. 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 the choice of sampling technique leads to significantly distinct estimates for both direct and net effects of the policy, as well as for the relevant network effects, such as homophily. Furthermore, using a modified diffusion contagion model, we show that our proposed sampling technique leads to greater and faster spread of the policy-linked behavior through the network. 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：评估嵌入社交网络中的受访者受政策干预的影响，往往因处理组内部及整个网络处理组与非处理组之间存在的网络干扰而具有挑战性。本文提出一种建模策略，将现有随机行为者导向模型（SAOM）与扩散传染模型相结合，并基于独立集识别提出一种新型网络抽样方法。通过将独立集中的受访者分配至处理组，我们能够阻断处理的直接溢出效应，从而将处理的直接效应与网络引发的间接效应分离。因此，本方法可在存在人群网络效应的条件下，同时估计选定政策干预的直接效应与净效应。我们通过比较模拟分析表明，抽样技术的选择会导致政策直接效应和净效应以及相关网络效应（如同质性）的估计值存在显著差异。此外，利用改进的扩散传染模型，我们证明所提出的抽样技术能促使政策相关行为在网络中更广泛、更迅速地传播。本研究凸显了网络抽样技术在优化政策评估研究中的重要性，有助于研究人员和政策制定者在网络化社会中更好地规划、设计并预判政策响应。