ArticleBiometrics2014
Estimating peer effects in longitudinal dyadic data using instrumental variables.
Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries.Social science & medicine (1982) · 2015Pooled it
- Addressing Causality and Homogeneity Assumptions in Exposure-Response Analyses.Clinical pharmacology and therapeutics · 2026Article
- Influence of multiple network structures on bayesian estimation of peer effects and statistical power for generalized linear network autocorrelation models.Applied network science · 2025Article
- Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples.Behavior genetics · 2023Article
- The diffusion of health care fraud: A bipartite network analysis.Social science & medicine (1982) · 2023Article
- Taking the problem of colliders seriously in the study of crime: A research note.Journal of experimental criminology · 2023Article
- Article
- Opening the Blackbox of Treatment Interference: Tracing Treatment Diffusion through Network Analysis.Sociological methods & research · 2022Article
- Social connections and the healthfulness of food choices in an employee population.Nature human behaviour · 2021Article
- The impact of sampling patients on measuring physician patient-sharing networks using Medicare data.Health services research · 2020Article
- Modeling peer effect modification by network strength: The diffusion of implantable cardioverter defibrillators in the US hospital network.Statistics in medicine · 2020Article
- Statistical methods for the estimation of contagion effects in human disease and health networks.Computational and structural biotechnology journal · 2020Review
- The Use of Instrumental Variables in Peer Effects Models.Oxford bulletin of economics and statistics · 2019Article
- Qualitative analysis of social network influences on quitting smoking among individuals with serious mental illness.Journal of mental health (Abingdon, England) · 2019Article
- The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Values, Norms, and Peer Effects on Weight Status.Journal of obesity · 2017Article
- Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.BMC medical research methodology · 2016Article
- The Cell as the First Niche Construction.Biology · 2016Article
- Social Relationships and Obesity: Benefits of Incorporating a Lifecourse Perspective.Current obesity reports · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.