Assessing intervention effects in a randomized trial within a social network

Shaina J. Alexandria*, Michael G. Hudgens, Allison E. Aiello

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.

Original languageEnglish (US)
Pages (from-to)1409-1419
Number of pages11
Issue number2
StatePublished - Jun 2023


  • causal inference
  • eX-FLU
  • interference
  • social distancing
  • spillover effect
  • stochastic potential outcomes

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability


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