Identifying causal estimates of peer-to-peer influence in networks is critical to marketing strategy, public policy and beyond. Unfortunately, separating correlation from causation in networked data is complicated. We argue that randomized experimentation in networks, made possible by the...

Understanding peer influence in networks is critical to estimating product demand and diffusion, creating effective viral marketing, and designing ‘network interventions’ to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer...