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Biometrics. 2014 Sep;70(3):506-15. doi: 10.1111/biom.12172. Epub 2014 Apr 29.

Estimating peer effects in longitudinal dyadic data using instrumental variables.

Author information

1
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, U.S.A.
2
Department of Sociology, Center for Demography and Ecology, University of Wisconsin-Madison, Madison, Wisconsin 53706, U.S.A.
3
Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts 02114, U.S.A.
4
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
5
Department of Sociology, Yale Institute for Network Science, Yale University, New Haven, Connecticut 06520, U.S.A.

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/alleles, or, more promisingly, time-varying gene expression, can identify peer effects if we instrument exclusion violations as well as the focal treatment. Third, we show that IV identification of peer effects remains possible even under multiple complications often regarded as lethal for IV identification of intra-individual effects, such as pleiotropy on observables and unobservables, homophily on past phenotype, past and ongoing homophily on genotype, inter-phenotype peer effects, population stratification, gene expression that is endogenous to past phenotype and past gene expression, and others. We apply our identification results to estimating peer effects of body mass index (BMI) among friends and spouses in the Framingham Heart Study. Results suggest a positive causal peer effect of BMI between friends.

KEYWORDS:

Body‐mass index; Causality; Directed acyclic graphs; Dyad; Genes; Homophily; Instrumental variable; Longitudinal; Mendelian randomization; Peer effect; Social network; Two‐stage least squares

PMID:
24779654
PMCID:
PMC4213357
DOI:
10.1111/biom.12172
[Indexed for MEDLINE]
Free PMC Article

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