Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

By Cosma Rohilla Shalizi, Andrew C. Thomas. 2010

1,060 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “For instance, 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).”
Regular Article (2004, neutral): “The lack of detailed information about the users prohibits us from assessing manifest homophily, and our analysis is thus subject to the problem of latent homophily which is generally confounded with contagion (8).”
The role of social networks in information diffusion (2012, background): “Statistical techniques such as permutation tests and matched sampling [5] help control for confounds, but ultimately cannot resolve this fundamental problem [39].”
Identity and Opinion: A Randomized Experiment (2014, neutral): “…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).”
Handbook of Big Data (2016, background): “Stepping up and down the ladder is achieved by repeatedly hitting • > to lower the threshold and obtain greater protection • to raise the threshold and lose protection.”
Estimating peer effects in networks with peer encouragement designs (2016, background): “581–590, and discussion (2013) 32(4):597–599.”
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior (2015, background): “In reality, all these effects are interconnected [9, 3] and are difficult to disentangle from observational data [35].”
Social contagion theory: examining dynamic social networks and human behavior (2011, background): “One paper in particular identifies two further, important assumptions that may be necessary or implicit in the directional test [77].”
Distinguishing between Personal Preferences and Social Influence in Online Activity Feeds (2016, background): “, [2]), a structural analysis of the causal graphical model [38] encoding influence and homophily shows that distinguishing them is impossible [42].”
The Book of Why: The New Science of Cause and Effect† (2019, background): “It is also difﬁcult to comprehend its nuanced refuta-tions by Lyons (2011) and by Shalizi and Thomas (2011).”
Is Gun Violence Contagious? A Spatiotemporal Test (2018, background): “, 2017), future work will likely need to confront the equivalent problem–separating non-random network clustering from non-random network diffusion (Shalizi and Thomas, 2011).”
A Survey on Causal Inference (2020, background): “The assumption of independent and identically distributed samples is ubiquitous in most causal inference methods, but this assumption cannot hold in many research areas, such as social media analytics [46] [123], herd immunity, and signal processing [140] [133].”