Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach

ArXi:2406.14380v5 Announce Type: replace-cross Online user-generated content platforms allocate billions of dollars of promotional traffic through algorithms in two-sided marketplaces. To evaluate updates to these algorithms, platforms frequently rely on creator-side randomized experiments. However, because treated and control creators compete for exposure, such experiments suffer from algorithmic interference: exposure outcomes depend on competitors' treatment status.