Adjusting for Peer-Influence in Propensity Scoring When Estimating Treatment Effects

Abstract

Analyses of treatments, experiments, policies, and observational data, are confounded when people's treatment outcomes and/or participation decisions are influenced by those of their friends and acquaintances. This invalidates standard matching techniques as estimation tools. For instance, the vaccination decisions of a person's peers affect the person's choice to vaccinate and the probability that the person is exposed to a disease (violating the usual Stable Unit Treatment Value Assumption). We account for these interferences by explicitly modeling peer interaction in treatment participation decisions, and balancing matchings to overcome correlation in outcomes. We incorporate these interaction effects into one of the most common techniques used to evaluate treatment effects: propensity score matching, and provide asymptotic results. We illustrate that peer-influenced propensity score matching gives more accurate results than standard propensity score matching in the estimation of the effectiveness of vaccinations and estimation of the impact of exercise participation on depression.