Yiyan Huang (黄一晏)
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Email: yiyhuang3-c@my.cityu.edu.hk Welcome to my homepage! I am a Postdoctoral researcher in the Department of Applied Mathematics at Hong Kong Polytechnic University (PolyU), host by Prof. Min Dai. I obtained my Ph.D. degree in Data Science from the School of Data Science at City University of Hong Kong (CityU), supervised by Prof. Qi Wu. I obtained my Bachelor's degree in Finance from the Department of Statistics and Finance at the University of Science and Technology of China (USTC). I was a visiting research scholar at the Department of Economics, Brown University, hosted by Prof. Toru Kitagawa. Welcome to contact me by email if you are interested in Causal Inference for everything (Business, Economics, Finance, Health, Engineering, Science, etc)! |
- 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. Representation Balancing with Decomposed Patterns 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, Shumin Ma, Qi Wu, Dongdong Wang, Zhixiang Huang. Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information. The 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI-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, Qi Wu, Dongdong Wang, Zhixiang Huang. Interpretable Causal Inference via Causal Graphs. 11th World Congress of the Bachelier Finance Society (abstract oral presentation at BFS-2022).
- 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).
- Yiyan Huang, Cheuk Hang Leung, Xing Yan, Qi Wu. Robust Causal Machine Learning of Treatment Effects. Data Mining and Knowledge Discovery (DMKD, Under review).
- Siyi Wang, Yiyan Huang (co-first author), Cheuk Hang Leung, Chaoqun Wang, Qi Wu. A Two-stage Disentangled and Balanced Representation Learning Method for Counterfactual Regression. Information Sciences (IS, Under review).
- Cheuk Hang Leung, Yiyan Huang (co-first author), Yijun Li, Qi Wu. Double Machine Learning for Time-Varying Continuous Treatment Effects. Journal of Business & Economic Statistics (JBES, Under review).
Research Interests
My research interests lie at the intersections of several disciplines, including Data Science, Causal Inference, Machine Learning, and Data-Driven Decision Making. Particularly, most of my work uses machine learning tools to estimate treatment effects in various fields and assist policy-makers in personalized decision makings.
Publications
Working Papers
Others
Internship at JD Digital Technology-Risk Management Center-Intelligent Model Department.
Reviewers for IJCAI2021, AISTATS2022, ICML2022, Neurips2022, ICML2023, Neurips2023, ICML2024, Neurips2024, AAAI2025, AISTATS2025, ICLR2025, ICML2025, Transactions on Machine Learning Research, etc.