Estimating Policy Effects in a Social Network with Independent Set Sampling
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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.
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