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...

Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect estimates obtained through Bernoulli randomized experiments are often biased due to violations of the...

Many internet firms use A/B tests to make product decisions. In an A/B test, the typical objective is to measure the total average treatment effect (TATE), which measures the difference between the average outcome if all users were treated and the average outcome if all users were untreated....

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...