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Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
Open accessAbstract
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 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
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“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
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“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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“Diffusion (or influence or contagion or induction) refers to the process by which characteristics or habits spread through a population (Shalizi & Thomas, 2011).”
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“…behavior clusters within a set of socially connected actors does not in and of itself imply the presence of a causal social diffusion process (Azoulay, Liu, and de Vaan and Stuart 583 Stuart 2017; Christakis and Fowler 2008; Liu et al. 2010; Shalizi and Thomas 2011; Van den Bulte and Lilien 2001).”
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“This possibility has been referred to elsewhere as an example of latent homophily bias (Shalizi and Thomas 2011) because it involves sorting on hard-to-observe characteristics that gives rise to a spurious association.”
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“Furthermore, Shalizi and Thomas (2011) demonstrated that, even if a baseline trait only affects friendship formation (not strength or duration), merely conditioning on the presence of a tie, which is inherent in all analyses focused on alter-ego pairs, creates a spurious association between the…”
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“Users with high betweenness values serve as information gatekeepers [36], the betweenness of a node is the number of the shortest paths between any two nodes in the network that have to pass through this node [37].”
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“Indeed, it can just be a reflection of the similarity between the involved individuals (Aral et al. 2009; Manski 1993; Shalizi and Thomas 2011) or a consequence of their intense interaction, since individuals that interact often have a greater probability to influence one another (Bakshy and Rosenn 2012).”
BackgroundAral, S. (2015) · doi:10.1093/oxfordhb/9780199948277.013.24 · Cited by 29
Cann, R. F., Sinnema, C., Daly, A. J., Rodway, J., & Liou, Y.-H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.775614 · Cited by 29
Bhattacharya, P., Phan, T. Q., Bai, X., & Airoldi, E. M. (2019) · Information Systems Research · doi:10.1287/isre.2018.0790 · Cited by 40
Firearm Violence as a Disease-"Hot People" or "Hot Spots"?
Branas, C., Jacoby, S. F., & Andreyeva, E. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.8273 · Cited by 28
Bruening, M. M., Ohri‐Vachaspati, P., Brewis, A., Laska, M. N., Todd, M. W., Hruschka, D. J., Schaefer, D. R., Whisner, C. M., & Dunton, G. F. (2016) · BMC Public Health · doi:10.1186/s12889-016-3536-5 · Cited by 30
“Because of the methodological limitations in previous studies, the mechanisms by which friends impact eating and PA behaviors and weight outcomes are not clear [21, 23, 24].”
Backgroundde Matos, M. G., Ferreira, P., Smith, M. D., & Telang, R. (2013) · Management Science · doi:10.1287/mnsc.2015.2258 · Cited by 35
“, 2009], [Aral, 2010], [Aral and Walker, 2011], [Shalizi and Thomas, 2011], and [Bapna and Umyarov, 2014]).”
Attributed graph clustering with subspace stochastic block model
Chen, H., Yu, Z., Yang, Q.-L., & Shao, J.-M. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.044 · Cited by 27
Rumormongering of genetically modified (GM) food on Chinese social network
Ji, J., Chao, N.-P., & Ding, J. (2019) · Telematics and informatics · doi:10.1016/j.tele.2019.01.005 · Cited by 27
Using Ego-Clusters to Measure Network Effects at LinkedInPreprint
Saint-Jacques, G., Varshney, M., Simpson, J., & Xu, Y. (2019) · arXiv.org · doi:10.48550/arxiv.1903.08755 · Cited by 27
“…multilevel designs [8, 20], cluster-based randomization, use of natural experiments [22], and model-assisted approaches specifying models for interference [21, 2, 4, 19, 7, 17] Observational analysis tends to be highly unreliable, because peer effects are often confounded with homophily [11, 13].”
Selection and Socialization Effects in Early Adolescent Alcohol Use: A Propensity Score Analysis
Scalco, M. D., Trucco, E. M., Coffman, D. L., & Colder, C. R. (2015) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-014-9969-3 · Cited by 32
“Recent claims that social network studies are confounded by latent homophily and social influence (Mouw 2006; Shalizi and Thomas 2011) may be directly tested by combining social network analysis with PSA and SEM.”
BackgroundMethodInfluentialModeling Dynamical Influence in Human Interaction Patterns
Pan, W., Cebrian, M., Dong, W., Kim, T., & Pentland, A. (2010) · arXiv.org · doi:10.48550/arxiv.1009.0240 · Cited by 27
“It has been recently shown that these mechanisms are generically confounded [11], but it is important to remember that this does not make observational data worthless.”
BackgroundMethodEthnic diversity fosters the social integration of refugee students
Boda, Z., Lorenz, G., Jansen, M., Stanat, P., & Edele, A. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01577-x · Cited by 39
Wang, X., Ow, T. T., Liu, L.-N., Feng, Y.-Q., & Liang, Y. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1782275 · Cited by 26
A Latent Space Network Model for Social Influence
Sweet, T. M., & Adhikari, S. (2020) · Psychometrika · doi:10.1007/s11336-020-09700-x · Cited by 26
Assortativity of suicide-related posting on social media.
Cero, I., & Witte, T. K. (2019) · American Psychologist · doi:10.1037/amp0000477 · Cited by 32
“Thus, although it is not currently possible to disentangle the causal mechanism of assortative patterns in nonexperimental network settings (Shalizi & Thomas, 2011), the current results have still narrowed the search.”
BackgroundForecasting elections using compartmental models of infections
Volkening, A., Linder, D. F., Porter, M., & Rempała, G. (2018) · SIAM Review · doi:10.48550/arxiv.1811.01831 · Cited by 26
“, adapting the same opinion) due to their shared characteristics [72], because of some external factors,”
Case studies in network community detection
Shai, S., Stanley, N., Granell, C., Taylor, D., & Mucha, P. (2017) · The Oxford Handbook of Social Networks · doi:10.48550/arxiv.1705.02305 · Cited by 26
“Generally speaking, studying the interplay between attribute information and network structure is complicated due to confounding effects (Shalizi and Thomas, 2011).”
BackgroundMultiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks
Evans, C. R., Onnela, J.-P. “., Williams, D. R., & Subramanian, S. V. (2016) · Social Science & Medicine · doi:10.1016/j.socscimed.2016.06.002 · Cited by 33
Selection and influence in cultural dynamics
Kempe, D., Kleinberg, J., Oren, S., & Slivkins, A. (2013) · ACM Conference on Economics and Computation · doi:10.1017/nws.2015.36 · Cited by 26
Peer Network Processes in Adolescents’ Health Lifestyles
Adams, J., Lawrence, E. M., Goode, J. A., Schaefer, D. R., & Mollborn, S. (2020) · Journal of Health and Social Behavior · doi:10.1177/00221465211054394 · Cited by 25
“Although theoretically appealing and frequently evoked to explain network similarity (Smith and Christakis 2008), influence is notoriously hard to identify empirically (Cohen-Cole and Fletcher 2008) and difficult to disentangle from homophilous selection (Shalizi and Thomas 2011).”
BackgroundWho is Spreading Rumours about Vaccines?: Influential User Impact Modelling in Social Networks
Kostkova, P., Mano, V., Larson, H., & Schulz, W. (2017) · Digital Humanities Conference · doi:10.1145/3079452.3079505 · Cited by 25
Transitions in Smokers’ Social Networks After Quit Attempts: A Latent Transition Analysis
Bray, B. C., Smith, R. A., Piper, M. E., Roberts, L. J., & Baker, T. B. (2016) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntw173 · Cited by 24
Selection effects in online sharing: consequences for peer adoption
Taylor, S. J., Bakshy, E., & Aral, S. (2013) · ACM Conference on Economics and Computation · doi:10.1145/2482540.2482604 · Cited by 25
Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks
Duong, C., Hoang, T. D., Nguyen, T., Jo, J., Hung, N. Q. V., & Aberer, K. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.04.036 · Cited by 24
Zorell, C. V. (2020) · Sustainability · doi:10.3390/su122410418 · Cited by 32
Rachiotis, G., Barbouni, A., Basagiannis, A., Katsioulis, A., Kostikas, K., Mouchtouri, V., Merakou, K., Kremastinou, J., & Hadjichristodoulou, C. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-034760 · Cited by 24
Joint Latent Space Model for Social Networks with Multivariate Attributes
Wang, S., Paul, S., & de Boeck, P. A. L. (2023) · Psychometrika · doi:10.1007/s11336-023-09926-5 · Cited by 19
“…1978; Leenders, 2002; Robins, Pattison and Elliott, 2001; Sweet and Adhikari, 2020; Frank, Zhao and Borman, 2004; Fujimoto, Wang and Valente, 2013; Shalizi and Thomas, 2011; VanderWeele, 2011; VanderWeele and An, 2013; Bramoullé, Djebbari and Fortin, 2009; Goldsmith-Pinkham and Imbens, 2013).”
Randomized experiments to detect and estimate social influence in networks
Taylor, S. J., & Eckles, D. (2017) · arXiv.org · doi:10.48550/arxiv.1709.09636 · Cited by 24
“A substantial program of research has been devoted to proving that what economists call “identification problems” in social influence are likely to be insurmountable without randomized experiments (Manski, 1993; Shalizi and Thomas, 2011).”
Backgroundvan der Leij, M. J. (2011) · Science · doi:10.1126/science.1214836 · Cited by 24
A Random-Field Approach to Inference in Large Models of Network Formation
Leung, M. P. (2019) · doi:10.2139/ssrn.2520272 · Cited by 24
“For example, Ciliberto and Tamer (2009) compute use simulated annealing, while Beresteanu et al.”
BackgroundMethodOpportunity without Organization: Labour Mobilization in Egypt after the 25th January RevolutionPreprint
Barrie, C., & Ketchley, N. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2997517 · Cited by 24
Network-Oriented Modeling and Its Conceptual Foundations
Jan Treur, A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-45213-5_1 · Cited by 21
Metadata
- Authors
- Shalizi, C. R., & Thomas, A. C.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Shalizi, Cosma Rohilla
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 40
- Issue
- 2
- Pages
- 211-239
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- PMC
- PMC3328971
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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