Yiyan Huang (黄一晏)

Research Interests

My primary research focuses on Causal Inference and Data Science, with a multidisciplinary approach integrating Statistics, Econometrics, Operations Research, and Machine Learning. I'm also interested in applying causal inference and data science methods for solving real-world problems in fields such as Management Science, Digital Marketing, Healthcare, etc. Currently, our group is focusing on three research lines (Collaborations are always welcome!):

⭐ 1. [Theoretical] Data-driven causal decision making: Studying the contextual and non-contextual bandit problem in different settings, in order to solve the resource allocation problem in Management Science and Operations Research.
⭐ 2. [Theoretical and Experimental] Causal4ML and ML4causal: Bridging the gap between causal inference and machine learning, aiming to address trustworthiness challenges such as robust learning, distribution shifts, and uncertainty.
⭐ 3. [Experimental] Reliable/Trustworthy LLM: Using causal inference and machine learning methods to build more reliable/trustworthy Large Language Models.

Publications (* indicates corresponding author)

• Yiyan Huang. Global Sensitivity in Private Policy Selection with Nuisance-Fitted Doubly Robust Scores. Transactions on Machine Learning Research (TMLR, 2026).
• Yiyan Huang*, Cheuk Hang Leung, Qi Wu, Zhiheng Zhang. Wasserstein Policy Learning for Distributional Outcomes. The 39th Annual Conference on Learning Theory (COLT 2026).
• Yuxuan Yang, Dugang Liu*, Yiyan Huang*. Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness. 32nd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2026).
• Jiacan Gao, Xinyan Su, Mingyuan Ma, Yiyan Huang, Xiao Xu, Xinrui Wan, Tianqi Gu, Enyun Yu, Jiecheng Guo, Zhiheng Zhang*. Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data. 43rd International Conference on Machine Learning (ICML-2026).
• Siyi Wang, Yiyan Huang, Cheuk Hang Leung, Chaoqun Wang, Qi Wu*. A Two-stage Disentangled and Balanced Representation Learning Method for Counterfactual Regression. Information Sciences, 2026.
• Cheuk Hang Leung, Yiyan Huang (co-first author) , Yijun Li, Qi Wu*. Distributionally Robust Off-Policy Evaluation and Learning with Continuous Treatments. The 39th AAAI Conference on Artificial Intelligence (AAAI-2025).
• Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li, Qi Wu*. Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators. The 38th Annual Conference on Neural Information Processing Systems (NeurIPS-2024).
• Yiyan Huang, Siyi Wang, Cheuk Hang Leung, Qi Wu*, Dongdong Wang, Zhixiang Huang. DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation. Transactions on Machine Learning Research (TMLR, 2024).
• Yijun Li, Cheuk Hang Leung, Xiangqian Sun, Chaoqun Wang, Yiyan Huang, Xing Yan, Qi Wu*, Dongdong Wang, Zhixiang Huang. The Causal Impact of Credit Lines on Spending Distributions. The 38th AAAI Conference on Artificial Intelligence (AAAI-2024).
• Shumin Ma, Zhiri Yuan, Qi Wu*, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, Zhixiang Huang. Deep into The Domain Shift: Transfer Learning through Dependence Regularization. IEEE Transactions on Neural Networks and Learning Systems (TNNLS, 2023).
• Yiyan Huang, Cheuk Hang Leung, Shumin Ma, Zhiri Yuan, Qi Wu*, Siyi Wang, Dongdong Wang, Zhixiang Huang. Towards Balanced Representation Learning for Credit Policy Evaluation. The 26th International Conference on Artificial Intelligence and Statistics (AISTATS-2023).
• Zhiri Yuan, Xixu Hu, Qi Wu*, Shumin Ma, Cheuk Hang Leung, Xin Shen, Yiyan Huang. A Unified Domain Adaptation Framework with Distinctive Divergence Analysis. Transactions on Machine Learning Research (TMLR, 2022).
• Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu*, Shumin Ma, Zhiri Yuan, Dongdong Wang, Zhixiang Huang. Robust Causal Learning for the Estimation of Average Treatment Effects. The 2022 International Joint Conference on Neural Networks (IJCNN-2022, oral).
• Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu*, Nanbo Peng, Dongdong Wang, Zhixiang Huang. The Causal Learning of Retail Delinquency. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-2021).

Journal Papers in Progress

Projects & Teaching & Services

主持国家自然科学基金青年项目、主持广东省地区培育项目、主持蚂蚁横向课题（因果AI决策）科研基金项目。

[2026 Spring] | Introduction to Computer Science | Undergraduate-level
[2026 Fall] | Introduction to Artificial Intelligence | Undergraduate-level

Area Chair / Reviewers for COLT、Neurips、ICML、ICLR、AAAI、AISTATS、IJCNN、ICASSP、TMLR、JMLR、TPAMI、Neural Networks、Journal of Biomedical Informatics, etc.