Estimating Peer-Influence Effects Under Homophily: Randomized Treatments and Insights

  • Niloy Biswas
  • Edoardo M. Airoldi
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


When doing causal inference on networks, there is interference among the units. In a social network setting, such interference among individuals is known as peer-influence. Estimating the causal effect of peer-influence under the presence of homophily presents various challenges. In this paper, we present results quantifying the error incurred from ignoring homophily when estimating peer-influence on networks. We then present randomized treatment strategies on networks which can help disentangle homophily from the estimation of peer-influence.


Causal inference Statistical network analysis Social networks Interference Randomized experiment Peer-influence Homophily 

Supplementary material


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of StatisticsHarvard UniversityCambridgeUSA

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