Finding influential subjects in a network using a causal framework

Youjin Lee, Ashley L. Buchanan, Elizabeth L. Ogburn, Samuel R. Friedman, M. Elizabeth Halloran, Natallia V. Katenka, Jing Wu, Georgios K. Nikolopoulos

Research output: Contribution to journalArticlepeer-review


Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

Original languageEnglish (US)
Pages (from-to)3715-3727
Number of pages13
Issue number4
StatePublished - Dec 2023


  • causal inference
  • centrality
  • contagion
  • interference

ASJC Scopus subject areas

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


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