BIOSTATISTICS SEMINAR
“Peer Effects and Interventions in Online Networks: With and
Without Experiments”
Dean Eckels, Scientist, Facebook
Peer effects (i.e., social interactions, interference, social influence, spillovers) are common in many
settings of interest to social scientists, epidemiologists, system designers, and policy-makers.
Researchers often aim to estimate these peer effects themselves and/or estimate what would happen
under a global (i.e., network-wide) treatment that functions partially through peer effects. In this
talk, I consider multiple strategies for learning about peer effects in online social networks using a
variety of experimental and non-experimental designs.
For estimating peer effects, we use experimental designs that either modulate mechanisms by which
peer effects occur or encourage peers to engage in the behaviors of interest. This is illustrated with
examples from online advertising and information sharing. (http://arxiv.org/abs/1206.4327)
When experimentation is not possible, observational analyses require often implausible assumptions
to identify peer effects. Nonetheless, adjustment and matching estimators may reduce bias enough
to be informative, if not unbiased. We use a large experiment that identifies peer effects in
information and media sharing as a "gold standard" for assessing the bias of observational studies of
peer effects. High-dimensional models adjusting for thousands of past behaviors provide the
greatest bias reduction, such that the full model reduces bias by over 70%.
For estimating effects of global treatments, estimates from simple random assignment and standard
analyses can suffer from substantial bias. We use experimental designs that reduce bias by
producing treatment assignments that are correlated in the network through the use of methods for
graph partitioning. We provide theoretical and simulation results showing this substantially reduces
bias and total error. (http://arxiv.org/abs/1404.7530)
Together this work illustrates the utility of large data sets, experimentation platforms, and modern
statistical learning for improving causal inference in social science and decision-making. This
covers joint work with Eytan Bakshy, Brian Karrer, Johan Ugander.
The Johns Hopkins Bloomberg School of Public Health, Department of
Biostatistics, Monday, December 1, 2014, 12:15-1:15, Room W3008,
School of Public Health (Refreshments: 12:00-12:15)
Note: Taking photos during the seminar is prohibited For disability access information or
listening devices, please contact the Office of Support Services at 410-955-1197 or on the Web at
www.jhsph.edu/SupportServices. EO/AA