Cited by 994Complex Network Analysis TechniquesAdvanced Causal Inference TechniquesSpatial and Panel Data Analysis

Homophily and contagion are generically confounded in observational social network studies

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Shalizi, Cosma Rohilla, Thomas, Andrew C.·2011·Sociological Methods & Research

Abstract

The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual’s covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.

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  • One solution to latent homophily bias is to model tie formation and dissolution explicitly, for example by measuring and conditioning on tie-forming characteristics of either or both individuals.

    “One solution to this problem is to model tie formation (and dissolution) explicitly (Shalizi & Thomas 2011). In the model of Figure 12, this can be accomplished by measuring and conditioning on the tie-forming characteristics of individual i, Ui, or individual j, Uj, or both.”

    From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011

    In “Contagion Effects Are Nonparametrically Unidentifiable” (page unavailable), the paper says that making the traits influencing tie formation observable can remove the confounding path, supporting the proposed conditioning on tie-forming characteristics. But it does not present explicitly modeling tie formation and dissolution alone as a solution: it says allowing ties to vary over time does not help, and describes successful explicit models as also requiring strong assumptions and adequate observed covariates. (LLM, conf=0.84)

    Experimental — accuracy not yet measured
  • Shalizi and Thomas showed that latent homophily bias is an example of endogenous selection bias, with the social tie as a pre-treatment collider.

    “Shalizi & Thomas (2011) recently showed that latent homophily bias constitutes a previously unrecognized example of endogenous selection bias, where the social tie itself plays the role of the pre-treatment collider.”

    From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011

  • Network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities.

    “network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”

    From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Shalizi and Thomas, 2011

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9 of the 63 references couldn’t be matched to a known paper.

Research works printed but not linked: 9

  1. Bakshy Eytan Brian Karrer Adamic Lada A. Chung J Fortnow L Pu P Social Influence and the Diffusion of User-Created Content 325 34 EC ’09: Proceedings of the Tenth ACM Conference on Electronic Commerce New York: ACM 2009
  2. Bourdieu Pierre Distinction:A Social Critique of the Judgement of Taste 1984 Harvard University Press Cambridge, MA
  3. Christakis Nicholas A. Fowler James H. The Spread of Obesity in a Large Social Network Over 32 Years The New England Journal of Medicine 2007 357 370 79 17652652
  4. Friedkin Noah E. A Structural Theory of Social Influence 1998 Cambridge University Press Cambridge, UK
  5. Gillespie John H. Population Genetics: A Concise Guide 1998 Johns Hopkins University Press Baltimore
  6. Huckfeldt Robert Johnson Paul E. John Sprague Political Disagreement: The Survival of Diverse Opinions within Communication Networks 2004 Cambridge University Press Cambridge, UK
  7. Leenders Roger T. A. J. Structure and Influence: Statistical Models for the Dynamics of Actor Attributes, Network Structure and Their Interdependence 1995 Thesis Publishers Amsterdam
  8. Lieberson Stanley Lynn Freda B. Barking Up the Wrong Branch: Scientific Alternatives to the Current Model of Sociological Science Annual Review of Sociology 2002 28 1 19
  9. Mayo Deborah G. Error and the Growth of Experimental Knowledge 1996 University of Chicago Press Chicago

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Authors
Shalizi, C. R., & Thomas, A. C.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Shalizi, Cosma Rohilla
Venue
Sociological Methods & Research
Published
2011-05
Volume
40
Issue
2
Pages
211-239
ISSN
  • 0049-1241 (ISSN-L)
  • 1552-8294
OpenAlex
W2149084727
Cited by
994
References
63
Citations by year
2026: 32 · 2025: 41 · 2024: 46 · 2023: 57 · 2022: 63 · 2021: 122 · 2020: 66 · 2019: 76 · 2018: 61 · 2017: 76 · 2016: 75 · 2015: 67 · 2014: 66 · 2013: 79 · 2012: 42
FWCI
50.87
Type
journal article
Language
en
Open access
Locations
  • Sociological Methods & Research (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University)
Funders
National Institute of Neurological Disorders and Stroke (R01NS047493)
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
homophily, social contagion, social influence, confounding, social networks, causal inference, imitation model, observational studies

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