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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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“The new influence estimation approach proposed here aims to attribute impact by accounting for causal narrative propagation over the network, while addressing the challenge of discriminating between actual social influence and mere homophily [29, 32].”
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Dafoe, A. (2018) · Sociological Methods & Research · doi:10.1177/0049124115613784 · Cited by 17
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The Spread of Physical Activity Through Social Networks
Stück, D., Hallgrímsson, H. T., Steeg, G. V., Epasto, A., & Foschini, L. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052688 · Cited by 18
Naimi, A. I. (2016) · Epidemiology · doi:10.1097/ede.0000000000000538 · Cited by 19
“Using directed acyclic graphs, it has been shown that homophily results in a d-connected path between the ego’s prior obesity status (the exposure) and the alter’s subsequent obesity state (the outcome).(39) In other words, the population of alters connected to obese egos may not be exchangeable with the population of…”
BackgroundCollective behaviour, uncertainty and environmental change
Bentley, R. A., & O’Brien, M. J. (2015) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2014.0461 · Cited by 18
The Big Impact of Small Groups on College Drinking
Fitzpatrick, B., Martinez, J., Polidan, E., & Angelis, E. (2015) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.2760 · Cited by 17
“Modeling this more complex longer term process may be rather challenging: Shalizi and Thomas (2011) note teasing out the causality of events arising from homophily and social influence is quite difficult.”
BackgroundPreentry Contacts and the Generation of Nascent Networks in Organizations
Sterling, A. D. (2015) · Organ. Sci. · doi:10.1287/orsc.2014.0921 · Cited by 17
“One reason for this may be the view that social structure is concomitant with quality; i.e., high-quality actors are assumed to be the ones with an early structural advantage (Barabási and Albert 1999, Mouw 2006, Shalizi and Thomas 2011).”
BackgroundSocial ties and concern for global warming
Itkonen, J. V. A. (2015) · Climatic Change · doi:10.1007/s10584-015-1424-0 · Cited by 19
“In principle, the association could be explained by a preference for like-minded friends (homophily), friends influencing each other (diffusion), or other factors influencing both friendship formation and concern for global warming (e.g., Kossinets and Watts 2009; Wimmer and Lewis 2010; Shalizi and Thomas 2011).”
BackgroundSeparating homophily and peer influence with latent spacePreprint
Davin, J., Gupta, S., & Piskorski, M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2373273 · Cited by 17
“…to control for latent traits X: Ŷit = b0 + b1Yi,t 1 + b2X̂i + b4 P j AijYj,t 1 P j Aij 3We tested a lagged variable in the data generating process with the same results 4Values from prior studies that use simulation studies to demonstrate bias in social influence studies (Shalizi & Thomas 2011).”
BackgroundMethodResultInfluentialElwert, F., Keller, T., & Kotsadam, A. (2023) · American Journal of Sociology · doi:10.1086/724865 · Cited by 16
“…unexpected challenges of identifying causal peer effects when social relations (e.g., network ties or contextual exposures) are not randomly formed by outside intervention (e.g., Manski 1993; Sobel 2006; Shalizi and Thomas 2011; VanderWeele and An 2013; Angrist 2014; Ogburn and VanderWeele 2014).”
BackgroundNeighborhood Adaptive Estimators for Causal Inference under Network Interference
Belloni, A., Fang, F., & Volfovsky, A. (2022) · arXiv.org · doi:10.48550/arxiv.2212.03683 · Cited by 16
“Moreover the individual specific î‹ ó There is a clear complication if the ǫ i are allowed to be dependent as differentiating between f ( ) and the error term would not necessarily be possible (Shalizi & Thomas 2011).”
BackgroundHierarchical attention network for attributed community detection of joint representation
Zhao, Q., Ma, H.-F., Guo, L., & Li, Z. (2022) · Neural computing & applications (Print) · doi:10.1007/s00521-021-06723-y · Cited by 16
“Principle of homophily and social influence [21, 24] have pointed out that nodes with similar attributes tend to connect to each other in a social network, offering an intuitive way to take attribute information into account for graph clustering.”
BackgroundChannels for Influence or Maps of Behavior? A Field Experiment on Social Networks and Cooperation
Atwell, P. C., & Nathan, N. L. (2021) · American Journal of Political Science · doi:10.1111/ajps.12586 · Cited by 16
Assortative interactions revealed by sorting of animal groups
Szorkovszky, A., Kotrschal, A., Herbert-Read, J., Buechel, S. D., Romenskyy, M., Rosén, E., van der Bijl, W., Pelckmans, K., Kolm, N., & Sumpter, D. (2018) · Animal Behaviour · doi:10.1016/j.anbehav.2018.06.005 · Cited by 16
Xu, R. (2018) · Social Networks · doi:10.1016/j.socnet.2018.01.002 · Cited by 17
Propensity score methodology in the presence of network entanglement between treatmentsPreprint
Toulis, P., Volfovsky, A., & Airoldi, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07310 · Cited by 16
“The combined causal effects from homophily and contagion are empirically indistinguishable without additional assumptions (Shalizi and Thomas, 2011).”
BackgroundBehavioral Communities and the Atomic Structure of Networks
Jackson, M., & Storms, E. C. (2017) · American Economic Journal: Microeconomics · doi:10.48550/arxiv.1710.04656 · Cited by 16
“This is a well-known confound of correlated behaviors (e.g., see Aral, Muchnik, and Sundararajan (2009); Shalizi and Thomas (2011); Jackson, Rogers, and Zenou (2017)).”
Causal Inference Under Network Interference: A Framework for Experiments on Social NetworksPreprint
Kao, E. (2017) · arXiv.org · doi:10.48550/arxiv.1708.08522 · Cited by 16
“…under interference, more recent work has shown how confounding social covariates may cause unidentifiability and biased estimates of causal effects (VanderWeele et al., 2014), especially when the social effects take place on a social network (Ogburn and VanderWeele, 2014; Shalizi and Thomas, 2011).”
BackgroundMethodInfluentialPeer influence as a potential magnifier of ADHD diagnosis.
Aronson, B. (2016) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2016.09.010 · Cited by 16
A Two-Stage Theory of Discussant Influence on Vote Choice in Multiparty Systems
Schmitt‐Beck, R., & Partheymüller, J. (2014) · British Journal of Political Science · doi:10.1017/s0007123414000301 · Cited by 23
“Many voters will not be able to construct congenial networks despite trying, since selectivity is constrained by the composition of the contexts out of which discussant can only be chosen.84 Although not 81 Shalizi and Thomas 2011.”
BackgroundModels, methods and network topology: Experimental design for the study of interference
Bowers, J., Desmarais, B. A., Frederickson, M. P., Ichino, N., Lee, H., & Wang, S. (2018) · Social Networks · doi:10.1016/j.socnet.2018.01.010 · Cited by 13
“Recent theoretical developments highlight the barriers to the identification of causal peer/contagion effects in networks with non-randomized, or observational, data (Lyons 2011; Shalizi and Thomas 2011).”
BackgroundEstimating the Effect of a Community-Based Intervention with Two Communities
van der Laan, M. V., Petersen, M., & Zheng, W.-J. (2013) · Journal of Causal Inference · doi:10.1515/jci-2012-0011 · Cited by 16
“, [25]).”
Engineering Social Contagions: Optimal Network Seeding in the Presence of HomophilyPreprint
Aral, S., Muchnik, L., & Sundararajan, A. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.1770982 · Cited by 16
Thomas, A. (2012) · Statistics in Medicine · doi:10.1002/sim.5551 · Cited by 16
“While each of these pieces has an explicit mechanism of causation (see Shalizi and Thomas [1] for a prior discussion), it is the possibility that the lion’s share of this autocorrelation is due to contagious spread, yielding the concept of “three degrees of influence”.”
BackgroundSelf-citationShifting frames: Collective action framing from a dialogic and relational perspective
Lindstedt, N. C. (2018) · Sociology Compass · doi:10.1111/soc4.12548 · Cited by 16
From Strangers to Friends: Tie Formations and Online Activities in an Evolving Social Network
Ameri, M., Honka, E., & Xie, Y. (2022) · Journal of Marketing Research · doi:10.1177/00222437221107900 · Cited by 15
“Multiple social phenomena can confound the identification of social influence (see Hartmann et al. 2008; Shalizi and Thomas 2011, and discussion in the “Identification” section).”
Influencers, Backfire Effects, and the Power of the Periphery
Centola, D. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.005 · Cited by 21
The Enormous Flock of Homophily Researchers: Assessing and Promoting a Research Agenda
McPherson, M., Smith‐Lovin, L., & Rawlings, C. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.033 · Cited by 12
Inferring mechanisms of response prioritization on social media under information overload
Gunaratne, C., Rand, W., & Garibay, I. (2021) · Scientific Reports · doi:10.1038/s41598-020-79897-5 · Cited by 15
Hopp, M. D. S., Stoeger, H., & Ziegler, A. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01929 · Cited by 17
Displaying things in common to encourage friendship formation: A large randomized field experiment
Sun, T., & Taylor, S. J. (2020) · Quantitative Marketing and Economics · doi:10.1007/s11129-020-09224-9 · Cited by 18
“This identification problem is fundamentally rooted in unobserved confounding present in observational data (Hartmann et al. 2008; Phan and Airoldi 2015; Kossinets and Watts 2009; Aral et al. 2009; Shalizi and Thomas 2011; Kwon et al. 2017)6.”
BackgroundInvestigating the association between social interactions and personality states dynamics
Gündoğdu, D., Finnerty, A. N., Staiano, J., Teso, S., Passerini, A., Pianesi, F., & Lepri, B. (2017) · Royal Society Open Science · doi:10.1098/rsos.170194 · Cited by 17
“Hence, just as contagion and homophily may be confounded in observational data [74], it may be the case that our complementarity effects may be confounded with a corresponding phenomena like heterophily (the tendency to interact preferentially with people who exhibit opposing behaviour or traits).”
BackgroundFinancing friends: How lobbyists create a web of relationships among members of Congress
Victor, J., & Koger, G. (2016) · Interest Groups & Advocacy · doi:10.1057/iga.2016.5 · Cited by 15
Sampling of Attributed Networks from Hierarchical Generative Models
Robles, P., Moreno, S., & Neville, J. (2016) · Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939808 · Cited by 15
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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