Design and Analysis of Experiments in Networks: Reducing Bias from Interference

@article{Eckles2014DesignAA,
  title={Design and Analysis of Experiments in Networks: Reducing Bias from Interference},
  author={Dean Eckles and Brian Karrer and Johan Ugander},
  journal={Journal of Causal Inference},
  year={2014},
  volume={5}
}
Abstract Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. [] Key Method In design, we consider the ability to perform random assignment to treatments that is correlated in the network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network neighbors.

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