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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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“, [2]), a structural analysis of the causal graphical model [38] encoding influence and homophily shows that distinguishing them is impossible [42].”
BackgroundMining Telecommunication Networks to Enhance Customer Lifetime Predictions
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“Social influence is a separate, but related concept explaining how individuals encourage similarity through their interactions [14].”
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Mosleh, M., Eckles, D., & Rand, D. G. (2025) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2404590122 · Cited by 14
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Urban segregation on multilayered transport networks: a random walk approach
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“Therefore, it is complicated to establish causality (Elwert and Winship 2014; Mouw 2006; Shalizi and Thomas 2011).”
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“Further, true random samples from the population of interest are not always easy to obtain; in fact, the process of gathering data is often far from ideal, as it may involve unknown sampling biases [10, 11, 59] or network effects [12, 60].”
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de la Haye, K., Shin, H., Yon, G. G. V., & Valente, T. W. (2019) · Journal of Health and Social Behavior · doi:10.1177/0022146519870521 · Cited by 16
“Hence, if friendship selection depended on some unobserved covariate that also predicted smoking initiation, our estimates would be biased because we would not be able to disentangle selection from influence (Shalizi and Thomas 2011).”
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“The social contagion (the effects caused by the people close to each other in a social network) are shown to be confounded by the effect of an individual’s covariates on his or her behavior or other measurable responses (Shalizi & Thomas, 2011).”
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“Moreover, homophily in unobserved attributes (latent homophily) that may be indicated by the very existence of a relationship of peers with a common friend (see Shalizi and Thomas 2011) will be equally represented in recipient and non-recipient peer groups.”
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“Under special conditions and with panel data this disentanglement is possible (see Bisin and Özgür, 2012; or Shalizi and Thomas, 2011 for a negative result in the most general case).4 We contribute to this literature bringing evidence on the importance of the elicitation of friendship network to…”
Inferring individual direct causal effects under heterogeneous peer influence
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“…works include causal diagrams for interference (Ogburn & VanderWeele, 2014), structural equation models (SEMs) (Ogburn et al., 2022), chain graphs (Shpitser et al., 2017; Ogburn et al., 2020), structural causal models (SCMs) at the unit level with linear structural equations (Zhang et al.,…”
Hasan, R., Cheyre, C., Ahn, Y., Hoyle, R., & Kapadia, A. C. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19295 · Cited by 16
“Establishing causal effects with observational data is challenging, as it is difficult to eliminate all non-causal explanations (Shalizi and Thomas 2011).”
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Franken, R., Bekhuis, H., & Tolsma, J. (2022) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2022.643150 · Cited by 13
“This means that the process of social influence, the confounding by shared exogenous social environments of social networkmembers, and endogenous social selection cannot be isolated from each other (Shalizi and Thomas, 2011).”
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“2013; Kossinets and Watts 2009; Shalizi and Thomas 2011).”
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Fairness in Networks: Social Capital, Information Access, and Interventions
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Prestige and homophily predict network structure for social learning of medicinal plant knowledge
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“For instance, Shalizi and Thomas (2011) argued that controlling for alter’s lagged obesity status may at best only partially account for homophily bias.”
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“Therefore, in future communication on the app they would likely have a shared starting point for connection and conversation and have a sense of familiarity which, in turn, could become a driver for future communication (Shalizi and Thomas, 2011).”
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“Recently, some researchers report the social theories related to topic categories in online social media, such as homophily (McPherson, Smith-Lovin, and Cook 2001), consistency (Abelson 1983) and contagion (Shalizi and Thomas 2011; Harrigan, Achananuparp, and Lim 2012).”
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“contemporaneous peer effects (Lyons 2011) and the identification of peer effects independent of homophily (Shalizi and Thomas 2011).”
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“Shalizi and Thomas (ST) illustrate how the confounding effects of latent homophily cause standard tests for contagion to fail with two examples [14].”
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“Yet, it is difficult to disentangle homophily from social influence [5, 6].”
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“…similar to themselves as friends, socialization, wherein individuals become more similar to their friends over time, or shared external causation (Shalizi and Thomas, 2011) Research in an early adolescent sample (ages 11 – 13) suggests that these results may also extend beyond domain-general…”
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“Firstly, researchers have been aware of the potential bias of results due to social homophily (Shalizi & Thomas, 2011).”
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“Addressing collider bias in the causal modelling of social network data and learning-based hypotheses presents additional challenges (Lyons, 2011; Shalizi & Thomas, 2011).”
Jiang, J., Dotsch, R., Roura, M. T., Liu, Y., Silva, V. D. C., Bos, M. W., & Barbieri, F. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580990 · Cited by 12
“Note that these two theories are not mutually exclusive nor conficting, although quantifying social contagion is challenging when it is usually unavoidably contaminated by network homophily [65].”
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Jiang, J., Chen, E., Luceri, L., Muri'c, G., Pierri, F., Chang, H.-C. H., & Ferrara, E. (2022) · arXiv.org · doi:10.36190/2023.02 · Cited by 11
“This process could unfold via either social homophily or social contagion or both (Shalizi and Thomas 2011).”
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“For instance, a study by Shalizi et al. on social network data has found that the use of regression coefficients, even taking into consideration temporal data, cannot distinguish causal factors from confounding variables [18].”
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“Observational social network studies have not gone without criticism, though, mainly due to them having difficulty separating causal social influence from other effects such as homophily (Shalizi and Thomas 2011) and context effects (Cohen-Cole and Fletcher 2008).”
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“Shalizi and Thomas (2011) point out challenges arising from the confounding of social contagion, homophily, and the influence of individual traits.”
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Zhou, J., Huang, D.-Y., & Wang, H.-S. (2016) · Science China Mathematics · doi:10.1007/s11425-015-0807-8 · Cited by 11
Life history factors, personality and the social clustering of sexual experience in adolescents
van Leeuwen, A. J., & Mace, R. H. (2016) · Royal Society Open Science · doi:10.1098/rsos.160257 · Cited by 12
“While friend similarities are often interpreted as evidence of peer effects, causality is hard to demonstrate because of alternative pathways to behavioural similarity, such as shared ecology and similarity-based assortment, that are often difficult to tell apart on the basis of observational data [76].”
BackgroundThreshold-Bounded Influence Dominating Sets for Recommendations in Social Networks
Eirinaki, M., Moniz, N., & Potika, K. (2016) · 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.67 · Cited by 11
“This line of work is drawing inspiration by social correlation theories such as homophily and social influence [1], [2].”
BackgroundAnalysis of link formation, persistence and dissolution in NetSense data
Bahulkar, A., Szymanski, B., Lizardo, O. A., Dong, Y.-X., Yang, Y., & Chawla, N. (2016) · International Conference on Advances in Social Networks Analysis and Mining · doi:10.48550/arxiv.1611.00568 · Cited by 11
Evaluating reproductive decisions as discrete choices under social influence
Bentley, R. A., Brock, W. A., Caiado, C., & O’Brien, M. J. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0154 · Cited by 8
“All too often, however, observational data alone fail to distinguish not only between social learning and individual learning, but also between social learning and homophily—the tendency for people with similar traits to co-associate [19,20].”
BackgroundSarkar, K., & Sundaram, H. (2013) · arXiv.org · doi:10.48550/arxiv.1303.5903 · Cited by 11
“However (Shalizi and Thomas 2011) have shown that homophily and social influence are generically confounded in social diffusion processes and it in general not easy to distinguish between the two effects.”
BackgroundMethodWhat’s in a Username? Identity Cue Effects in Social Media
Taylor, S. J., Muchnik, L., & Aral, S. (2014) · doi:10.2139/ssrn.2538130 · Cited by 10
“…as with earlier work on peer effects in economics, causal inference for effects of online social content is difficult due to endogeneity in which users see signals from whom (Shalizi and Thomas 2011) as well as which users decide to generate social content (Toubia and Stephen 2013, Taylor et al. 2013).”
Let There Be Claws: An Early Social Network Analysis of AI Agents on MoltbookPreprint
Price, H. C. W., AlMuhanna, H., Bassani, P. M., Ho, M., & Evans, T. S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2602.20044 · Cited by 14
“When global patterns emerge, their origin is often contested (Watts and Strogatz; 1998; Shalizi and Thomas; 2011).”
Controlling Homophily in Social Network Regression Analysis by Machine Learning
Liu, X., & Huang, K.-W. (2024) · INFORMS journal on computing · doi:10.1287/ijoc.2022.0287 · Cited by 10
Wolf, S. A., Heerdink, M. W., & Kleef, G. A. V. (2023) · Emotion Review · doi:10.1177/17540739231182241 · Cited by 12
“…of initial, pre-exposure agreement that remains stable over time, for example, all group members expressing happiness instantaneously due to shared social identities and group-based appraisals or homophily and similar response tendencies (see Figure 1; Mackie & Smith, 2015; Shalizi & Thomas, 2011).”
BackgroundWiddershoven, S., Pluymaekers, M., Zourrig, H., Sinclair, P., & Bloemer, J. (2023) · International Journal of Business Communication · doi:10.1177/23294884231200244 · Cited by 10
“In fact, some authors have argued that in observational studies, contagion effects cannot be reliably differentiated from the effects of homophily, which refers to the tendency of individuals to select friends who resemble them (Shalizi & Thomas, 2011).”
BackgroundA Theoretical Examination of Homophily Beyond Focus Theory: Causes, Consequences, and New Directions
Au, A. (2023) · SAGE Open · doi:10.1177/21582440231173915 · Cited by 13
“Scarce, but important network studies have refined the argument to show how certain types of homogeneous relationships, such as work relationships, are especially conducive for fostering other types of relationships, like friendships (Liberman et al., 2021; Shalizi & Thomas, 2011).”
BackgroundOuyang, Y.-Q., Ouyang, Z., & Xu, X. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20032005 · Cited by 10
“The theory of secondary homophily holds that [29] adolescents tend to befriend peers with the same ethnic and racial backgrounds [30], similar school grades [31,32], and similar socioeconomic status [33] and these attributes potentially intersect with educational expectations, and might increase the expectation-relate…”
BackgroundCommunity detection in subspace of attribute
Chen, H., Yu, Z., Yang, Q.-L., & Shao, J.-M. (2022) · Information Sciences · doi:10.1016/j.ins.2022.04.047 · Cited by 10
Silk, M., Carrignon, S., Bentley, R., & Fefferman, N. (2022) · BMC Public Health · doi:10.1186/s12889-021-12353-9 · Cited by 10
Network Effects Research: A Systematic Review of Theoretical Mechanisms and Measures
Medina, A., Siciliano, M. D., Wang, W., & Hu, Q. (2021) · American Review of Public Administration · doi:10.1177/02750740221118825 · Cited by 10
“With crosssectional data, it is impossible to differentiate between selection and influence (Shalizi and Thomas 2010).”
BackgroundInference and influence of network structure using snapshot social behavior without network data
Godoy-Lorite, A., & Jones, N. S. (2021) · Science Advances · doi:10.1126/sciadv.abb8762 · Cited by 10
Cheating in online gaming spreads through observation and victimization
Kim, J. E., & Tsvetkova, M. (2021) · Network Science · doi:10.1017/nws.2021.19 · Cited by 14
“Real social networks present three potential confounders to social influence [49, 50].”
BackgroundDaza, S., & Kreuger, L. (2019) · Sociological Methods & Research · doi:10.1177/0049124119826147 · Cited by 10
“Unfortunately, disentangling selection and social influence have proven to be extremely difficult (Shalizi and Thomas 2011; VanderWeele 2011).”
BackgroundBias and beyond in digital trace data
Malik, M. M. (2018) · doi:10.1184/r1/7188947.v1 · Cited by 10
Bissell, P., Peacock, M., Holdsworth, M., Powell, K., Wilcox, J., & Clonan, A. (2018) · Sociology of Health and Illness · doi:10.1111/1467-9566.12746 · Cited by 10
An efficient counting method for the colored triad census
Lienert, J., Koehly, L., Reed-Tsochas, F., & Marcum, C. S. (2019) · Social Networks · doi:10.1016/j.socnet.2019.04.003 · Cited by 8
Peer pressure enables actuation of mobility lifestyles
Feygin, S. A., & Pozdnoukhov, A. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.12.008 · Cited by 10
“A group of friends protesting a mutual acquaintance’s decision to buy a fuel-inefficient SUV can be seen as 1 See Christakis and Fowler (2013) and Shalizi and Thomas (2011) for a recent exploration of the identification problem in the econometric analysis of diffusive processes in social networks,…”
Are ‘Water Smart Landscapes’ Contagious? An Epidemic Approach on Networks to Study Peer Effects
Brelsford, C., & Bacco, C. D. (2018) · Networks and Spatial Economics · doi:10.1007/s11067-018-9416-z · Cited by 10
Topical Alignment in Online Social Systems
Cardoso, F. M., Meloni, S., Santanchè, A., & Moreno, Y. (2017) · Frontiers of Physics · doi:10.3389/fphy.2019.00058 · Cited by 10
“, thus, it might be impossible to verify whether ties similarity is really the result of homophily or social influence [24].”
BackgroundAspect-Level Influence Discovery from Graphs
Hu, C., & Cao, H. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2538223 · Cited by 7
“Several commonly recognized influence factors in social sciences include homophily, contagion, and causality [27], [29] where homophily captures the similarities among social”
BackgroundA framework for analyzing and detracting negative emotional contagion in online social networks
AlSagri, H. S., & Ykhlef, M. (2016) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2016.7476096 · Cited by 9
“Dividing graphs into homogeneous, densely inter-connected clusters of nodes, with minimal connection between clusters has been the topic of community discovery [31], [35], [39][42].”
Shadow networks: Discovering hidden nodes with models of information flowPreprint
Bagrow, J. P., Desu, S., Frank, M. R., Manukyan, N., Mitchell, L., Reagan, A. J., Bloedorn, E., Booker, L. B., Branting, L. K., Smith, M. J., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6122 · Cited by 9
“In the case of public health policy, for example, positive evidence for the role of social contagion in the spreading of such disparate attributes as happiness [20], obesity [21], and loneliness [22], have been challenged due to their reliance on under-sampled reconstructed social networks [23].”
BackgroundMyneni, S., Fujimoto, K., & Cohen, T. (2017) · Health informatics · doi:10.1007/978-3-319-51732-2_15 · Cited by 10
A causal framework for the drivers of animal social network structure
Kawam, B., Ostner, J., McElreath, R. L., Schülke, O., & Redhead, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013370 · Cited by 12
The gendered maths confidence gap, social influence and social integration
Raabe, I. J., & Block, P. (2024) · European Societies · doi:10.1080/14616696.2024.2349217 · Cited by 10
Causal inference over stochastic networks.
Clark, D. A., & Handcock, M. (2024) · Journal of the Royal Statistical Society. Series A, · doi:10.1093/jrsssa/qnae001 · Cited by 9
“…is an extensive literature on causal inference methods for so called “network settings” from a variety of perspectives (Hudgens and Halloran, 2008; Shalizi and Thomas, 2011; Ogburn and VanderWeele, 2014; van der Laan, 2014; Sofrygin and van der Laan, 2017; DeAmour, 2016; Aronow and Samii, 2017;…”
BackgroundInfluentialDiffusion in small worlds with homophily and social reinforcement: A theoretical model
Tur, E. M., Zeppini, P., & Frenken, K. (2024) · Soc. Networks · doi:10.1016/j.socnet.2023.05.004 · Cited by 9
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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