Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems
This article presents a comprehensive overview of how causal inference techniques are applied to identify and correct various biases in Kuaishou's recommendation pipeline, covering background theory, recent research, practical implementations such as popularity debias, causal embedding decoupling, and video completion‑rate debias, along with experimental results and future challenges.
