Bibliographic Details
| Title: |
Estimating policy effects in a social network with independent set sampling. |
| Authors: |
Ang, Eugene T.Y. (AUTHOR), Bhattacharya, Prasanta (AUTHOR), Lim, Andrew E.B. (AUTHOR) |
| Source: |
Social Networks. May2025, Vol. 81, p17-30. 14p. |
| Subjects: |
NETWORK effect, TREATMENT effectiveness, INDEPENDENT sets, SOCIAL networks, STOCHASTIC models |
| 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. 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. • A policy evaluation technique that samples individuals from an independent set. • Leverages an SAOM and a counterfactual design to isolate direct policy effect from network effect. • Illustrative studies using real world and simulated network data to demonstrate effectiveness. • Offers a way to infer direct and net effect of interventions in a social network. [ABSTRACT FROM AUTHOR] |
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| Database: |
Regional Business News |
