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

Résumé 0

Recommender systems are essential for content-sharing platforms by curating personalized content. To evaluate updates of recommender systems targeting content creators, platforms frequently engage in creator-side randomized experiments to estimate treatment effect, defined as the difference in outcomes when a new (vs. the status quo) algorithm is deployed on the platform. We show that the standard difference-in-means estimator can lead to a biased treatment effect estimate. This bias arises because of recommender interference, which occurs when treated and control creators compete for exposure through the recommender system. We propose a "recommender choice model" that captures how an item is chosen among a pool comprised of both treated and control content items. By combining a structural choice model with neural networks, the framework directly models the interference pathway in a microfounded way while accounting for rich viewer-content heterogeneity. Using the model, we construct a double/debiased estimator of the treatment effect that is consistent and asymptotically normal. We demonstrate its empirical performance with a field experiment on Weixin short-video platform: besides the standard creator-side experiment, we carry out a costly blocked double-sided randomization design to obtain a benchmark estimate without interference bias. We show that the proposed estimator significantly reduces the bias in treatment effect estimates compared to the standard difference-in-means estimator.