Estimating peer effects in longitudinal dyadic data using instrumental variables.

ABSTRACT: The identification of causal peer effects (also known as social contagion or induction) from observational data in social networks is challenged by two distinct sources of bias: latent homophily and unobserved confounding. In this paper, we investigate how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental variables (IV) in a large set of data generating models with homophily and confounding. We use directed acyclic graphs to represent these models and employ multiple IV strategies and report three main identification results. First, using a single fixed gene (or allele) as an IV will generally fail to identify peer effects if the gene affects past values of the treatment. Second, multiple fixed genes/allel

SUBMITTER: O'Malley AJ 

PROVIDER: S-EPMC4213357 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature