  Peer effects, in which the behavior of an individual is affected by the
behavior of their peers, are posited by multiple theories in the social
sciences. Other processes can also produce behaviors that are correlated in
networks and groups, thereby generating debate about the credibility of
observational (i.e. nonexperimental) studies of peer effects. Randomized field
experiments that identify peer effects, however, are often expensive or
infeasible. Thus, many studies of peer effects use observational data, and
prior evaluations of causal inference methods for adjusting observational data
to estimate peer effects have lacked an experimental "gold standard" for
comparison. Here we show, in the context of information and media diffusion on
Facebook, that high-dimensional adjustment of a nonexperimental control group
(677 million observations) using propensity score models produces estimates of
peer effects statistically indistinguishable from those from using a large
randomized experiment (220 million observations). Naive observational
estimators overstate peer effects by 320% and commonly used variables (e.g.,
demographics) offer little bias reduction, but adjusting for a measure of prior
behaviors closely related to the focal behavior reduces bias by 91%.
High-dimensional models adjusting for over 3,700 past behaviors provide
additional bias reduction, such that the full model reduces bias by over 97%.
This experimental evaluation demonstrates that detailed records of individuals'
past behavior can improve studies of social influence, information diffusion,
and imitation; these results are encouraging for the credibility of some
studies but also cautionary for studies of rare or new behaviors. More
generally, these results show how large, high-dimensional data sets and
statistical learning techniques can be used to improve causal inference in the
behavioral sciences.
