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 choices and/or outcomes 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 as well as 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 choices and then balance matchings accordingly. We incorporate this approach into one of the most common techniques used to evaluate treatment effects - propensity score matching. Empirical illustrations show that peer-influenced propensity score matching gives more accurate results than standard propensity score matching in the estimation of the effectiveness of vaccinations as well as the impact of exercise participation on depression.

Keywords: Peer-Influenced Propensity Score Matching (PIPSM); Peer Influence; Propensity Scores; Matched Samples; Matching; Treatment Effect; Influence Network; Peer Effect; Exercise; Depression

JEL Classification: C31; C35; C57; D85, I12