Estimating Policy Effects in a Social Network with Independent Set Sampling

TL;DR

This paper introduces a new method combining independent set sampling and stochastic actor-oriented models to accurately estimate policy effects in social networks, effectively addressing network interference issues.

Contribution

It proposes a novel empirical strategy that isolates direct policy effects by using independent set sampling to block spillovers in social networks.

Findings

Effective in blocking direct spillover effects

Accurate estimation of policy impact demonstrated through simulations

Network sampling influences policy effect evaluation

Abstract

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 novel empirical strategy that combines network sampling based on the identification of independent sets with a stochastic actor-oriented model (SAOM) to infer the direct and net effects of a policy. By assigning respondents from an independent set to the treatment, we are able to block direct spillover of the treatment among the treated respondents for an extended period of time, during which the direct effect of the treatment can be isolated from the associated network interference. We empirically demonstrate this using a simulation-based evaluation of a fictitious policy implementation using both real-life and generated networks, and use a counterfactual approach to estimate the treatment effect of the policy. Our results highlight the effectiveness of our proposed empirical strategy, and notably, the role of network sampling techniques in influencing the evaluation of policy effects. The findings from this study have the potential to help researchers and policymakers with planning, designing, and anticipating policy responses in a networked society.