Abstract
When the treatment assignment of one unit affects the outcome of another, we say there is interference. Interference is especially prevalent in marketplaces, where buyer and seller interactions lead to complex dependence structures. As a violation of the stable unit treatment value assumption (SUTVA), the presence of interference can lead to bias of standard estimators under naive randomized designs. In this talk, we will cover a set of design and estimation paradigms to conduct causal inference research in a bipartite graph setting, inspired from (but not limited to) marketplace experiments, with specific attention to clustered randomized designs under different randomization constraints and bias corrections to standard estimators
Bio
http://jean.pouget-abadie.com/
