  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.
