Causal Transformer Networks for Counterfactual Reasoning in Large-Scale Recommendation Systems

Abstract

Modern recommendation systems suffer from popularity bias, filter bubbles, and spurious correlations that degrade long-term user satisfaction. We introduce CausalRec, a Transformer-based architecture that integrates structural causal models into the attention mechanism, enabling counterfactual reasoning at inference time: "Would the user have clicked this item if it were not promoted on the homepage?" Deployed in a 28-day A/B test on a major e-commerce platform (430 million daily active users), CausalRec increases 30-day user retention by 3.8%, reduces popularity bias Gini coefficient by 22%, and improves content diversity by 31% while maintaining gross merchandise value (GMV) parity.