周晓华教授在人工智能领域，特别是在推荐系统、大语言模型（LLM）以及医疗数据的统计学习方面，作出了重要成果。周晓华教授将因果推断方法引入推荐系统算法，提出基于因果框架的模型，以提升推荐的准确性、公平性和可解释性；同时探索了将因果统计推理与大规模语言模型相结合的方法，以增强模型的稳健性和可解释性。他的研究有效融合了人工智能技术与统计理论，推动了可解释智能系统的发展。

部分发表文章：

1. Li X, Zhou XH*. Temporal visiting-monitoring feature interaction learning for modelling structured electronic health records. Knowledge-Based Systems [Internet]. 2025 Jul 23;327:114155

2. Li HX, Zheng CY, Wang S, Wu K, Wang E, P Wu,  Geng Z, Chen X, Zhou XH*. Relaxing the Accurate Imputation Assumption. Proceedings of the 41st International Conference on Machine Learning, 2024

3. Li HX, Zheng CY, Wang W,  Wang E, Feng F, and Zhou XH*. 2024. Denoising Recommendation on Data Missing Not at Random. .In Proceedings of August 25–29, 2024 (KDD). ACM, New York, NY, USA

4. Hu T and Zhou XH*. Debiasing the Conversion Rate Prediction Model in the Presence of Delayed Implicit Feedback. Entropy 2024 Sep 15;26(9):792

5. Li HX , Dai Q,  Li Y, Lyu Y,  Dong ZH, Zhou XH, Wu P. Multiple Robust Learning for Recommendation. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) 2023

6. Wu P, Li H, Deng Y, Hu W, Dai Q, Dong Z*, Sun J, Zhang R, Zhou XH*. On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges. In International Joint Conferences on Artificial Intelligence. 2022