Education

Sep. 2014 – Jul. 2019, Ph.D. in Statistics, Peking University

Sep. 2010 – Jul. 2014, B.S. in Statistics, Peking University

Work Experience

Aug. 2024 – Present, Associate Professor, School of Statistics, Renmin University of China

Sep. 2021 – Aug. 2024, Lecturer, School of Statistics, Renmin University of China

Jul. 2019 – Aug. 2021, Senior Engineer, Huawei Noah’s Ark Lab

Research Interests

Causal Inference

Trustworthy Machine Learning

Funding

Title: Causal Effect Estimation Algorithms Based on Graphical Models and Invariant Causal Prediction

Funding: National Natural Science Foundation of China – Youth Fund

Role: Principal Investigator

Start Date: Jan. 2022

Publications

· Geng, Z., Liu, Y., Liu, C. C., & Miao, W. (2019). Evaluation of causal effects and local structure learning of causal networks. Annual Review of Statistics and Its Application, 6, 103–124.

· Liu, Y., Zheng, C., Liu, C. C., & Geng, Z. (2019). Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths. ACM Transactions on Intelligent Systems and Technology (TIST), 10, 49:1–49:15.

· Liu, Y., Fang, Z., He, Y., Geng, Z., & Liu, C. (2020). Local causal network learning for finding pairs of total and direct effects. Journal of Machine Learning Research (JMLR).

· Fang, Z., Liu, Y., Geng, Z., Zhu, S., & He, Y. (2022). Local Method for Identifying Causal Relations under Markov Equivalence. Artificial Intelligence (AIJ).

· Li, H., Zheng, C., Cao, Y., Geng, Z., Liu, Y., & Wu, P. (2023). Trustworthy Policy Learning under the Counterfactual No-Harm Criterion. ICML.

· Li, H., Zheng, C., Wu, P., Kuang, K., Liu, Y., & Cui, P. (2023). Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. KDD.

· Kuang, K., Wang, H., Liu, Y., Xiong, R., Wu, R., & Lu, W. (2023). Stable Prediction With Leveraging Seed Variable. IEEE Transactions on Knowledge and Data Engineering (TKDE).

· Fang, Z., Zhu, S., Zhang, J., Liu, Y., Chen, Z., & He, Y. (2023). Low rank directed acyclic graphs and causal structure learning. IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

· Li, H., Liu, Y., Geng, Z., & Zhang, K. (2024). A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs. NeurIPS.

· Li, H., Tang, Z., Jiang, Z., Fang, Z., Liu, Y., Geng, Z., & Zhang, K. (2025). Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness. ICML.

Patents

· A Trustworthy Policy Learning Method and Device Satisfying the Counterfactual No-Harm Criterion, ZL 2023 1 0916949.5. Inventors: Yue Liu, Chunyuan Zheng, Haoxuan Li, Peng Wu, Yixiao Cao, Zhi Geng. Affiliation: Renmin University of China, 2023.

· Method, System, and Device for Training and Predicting with a Counterfactually Fair Model, ZL 2023 1 1236303.9. Inventors: Yue Liu, Haoxuan Li, Chunyuan Zheng, Zhi Geng, Kun Zhang. Affiliation: Renmin University of China, 2025.

Teaching

Undergraduate: Mathematical Analysis I, II, III, Selected Topics in Modern Mathematics

Graduate: Selected Topics in Machine Learning