A Survey on Causal Inference‐Driven Data Bias Optimization in Recommendation Systems: Principles, Opportunities and Challenges

ABSTRACT Recommendation systems predict user interests and recommend items for online platforms including e‐commerce, social networks, and decision systems. However, data bias has become a significant obstacle, severely impacting the accuracy, fairness, and reliability of recommendation results. This survey examines causal inference for optimizing recommendation systems and mitigating data bias, addressing three questions: (1) Bias types and performance impacts; (2) Causal inference mitigation methods; (3) Approach advantages, limitations, and research opportunities. The motivation for this survey stems from the limitations of traditional debiasing methods, which often fail to account for causal relationships and struggle in dynamic, real‐world scenarios. Causal inference provides a robust framework for identifying and addressing the underlying causes of bias, enabling more transparent and accurate recommendation systems. Therefore, we define three critical stages of bias: bias in the data stage, model selection stage, and model evaluation stage. For each stage, causal inference‐based optimization methods are introduced and critically analyzed. Unlike traditional debiasing methods, this study analyzes data augmentation and regularization techniques as potential strategies for future research. The whole research might highlight the ability of causal inference to uncover and control confounding factors, offering deeper insights into the mechanisms driving biases.