title: Estimating peer effects in networks with peer encouragement designs.
abstract: Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are central to social science. Because peer effects are often confounded with homophily and common external causes, recent work has used randomized experiments to estimate effects of specific peer behaviors. These experiments have often relied on the experimenter being able to randomly modulate mechanisms by which peer behavior is transmitted to a focal individual. We describe experimental designs that instead randomly assign individuals' peers to encouragements to behaviors that directly affect those individuals. We illustrate this method with a large peer encouragement design on Facebook for estimating the effects of receiving feedback from peers on posts shared by focal individuals. We find evidence for substantial effects of receiving marginal feedback on multiple behaviors, including giving feedback to others and continued posting. These findings provide experimental evidence for the role of behaviors directed at specific individuals in the adoption and continued use of communication technologies. In comparison, observational estimates differ substantially, both underestimating and overestimating effects, suggesting that researchers and policy makers should be cautious in relying on them.
authors.affiliation: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02139; Facebook, Menlo Park, CA 94025; dean@deaneckles.com.
authors.forename: Dean
authors.initials: D
authors.lastname: Eckles
authors.affiliation: Facebook, Menlo Park, CA 94025; Department of Communication, Stanford University, Stanford, CA 94305.
authors.forename: René F
authors.initials: RF
authors.lastname: Kizilcec
authors.affiliation: Facebook, Menlo Park, CA 94025;
authors.forename: Eytan
authors.initials: E
authors.lastname: Bakshy
mesh_terms: D006801:Humans; D010379:Peer Group; D060756:Social Networking; D012944:Social Support
keywords: causal inference; experimental design; social interactions; social networks