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September 5, 2025Journal of the Royal Statistical Society Series A (Statistics in Society)Open Access

A Bayesian approach to estimate causal peer influence accounting for latent network homophily

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Authors

SUSeungha UmTSTracy M. SweetSASamrachana Adhikari

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Overview

Bayesian approach reveals peer influence effects in social networks, accounting for homophily challenges.

Key Points

  • Latent homophily can significantly distort estimates of causal peer influence between individuals in social networks.
  • Utilizing simulations, the study demonstrates that accounting for homophily leads to more accurate estimations of peer influence.
  • A Bayesian hierarchical modelling framework enables the analysis of multiple networks efficiently, enhancing understanding of peer dynamics.
  • The findings highlight the necessity of incorporating latent factors to improve the precision of causal inference in social sciences.

Cite This Study

Um et al. (2025) studied this question.

synapsesocial.com/papers/68bb49d26d6d5674bccfffa1https://doi.org/10.1093/jrsssa/qnaf131
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