I am an Assistant Professor in Statistics in the Donald Bren School of Information and Computer Sciences at University of California Irvine. I obtained my Ph.D. degree in Statistics at North Carolina State University (NCSU), co-advised by Dr. Wenbin Lu and Dr. Rui Song. Prior to that, I obtained a B.S. in Statistics from Zhejiang University in July 2017.
I have broad research interests in methodology and theory in causal inference, reinforcement learning, graphical model, and their interchanges, to establish reliable, powerful, and interpretable solutions to wide real-world problems. Currently, my main research work includes individualized optimal decision making with complex data, policy evaluation in reinforcement learning and bandits, natural language processing and explainable deep learning, and causal discovery for high-dimensional individual mediation analysis, directly motivated by precision medicine, customized economics, personalized marketing, modern epidemiology, etc.
Click here for my name in Chinese and how to pronounce it.
Contact me: hengrc1@uci.edu
PhD in Statistics, 2022
North Carolina State University
B.S. in Statistics, 2017
Zhejiang University
Feb 2024: Our tutorial book on Causal Decision Making is online! Check https://causaldm.github.io/Causal-Decision-Making/.
Jan to March 2024: I will present my works at AAAI-2024 Tutorial, SEEDS-2024, SSC-2024, EcoStat 2024, ACIC, UC San Diego.
Jan 2024: Our work On Efficient Inference of Causal Effects with Multiple Mediators is available on ArXiv.
Dec 2023: Our work Is Knowledge All Large Language Models Needed for Causal Reasoning? is available on ArXiv.
Dec 2023: Congratulations to my student Wenbo Zhang on passing the Ph.D. Advancement to Candidacy with the title Towards Trustworthy Machine Learning: A Lens on Explainability !
Oct 2023: Our tutorial proposal on Foundations, Practical Applications, and Latest Developments in Causal Decision Making is accepted at AAAI-2024 Tutorial!
Oct to Dec 2023: I shared my recent works at INFORMS Annual Meeting, NeurIPS, and CMStatistics.
Sept 2023: Our paper On Learning Necessary and Sufficient Causal Graphs is accepted at NeurIPS 2023 as a spotlight!
Sept 2023: Our paper Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning is accepted at Journal of the American Statistical Association.
May to Sep 2023: I shared my recent works at ICSA, RUC IFS, JSM, EcoSta, Stat@UofWaterloo, PCIC.
April 2023: Our paper Towards Trustworthy Explanation: On Causal Rationalization is accepted at ICML 2023. Congrats to my student Wenbo Zhang !
April 2023: Our paper On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs is accepted at ICML 2023.
Jan to April 2023: I shared my recent works at RL+X Seminar, BrownBag Seminar, CS@UCSB, Stat@SDSU, CS@UCI.
Jan 2023: Our paper Jump Q-Learning for Individualized Decision Making with Continuous Treatments is published at Journal of Machine Learning Research.
(* co-first author, ___ graduate student author, ^ corresponding author)
Cai, H., Wang, Y., Jordan, M., & Song, R. (2023). On Learning Necessary and Sufficient Causal Graphs. Advances in Neural Information Processing Systems (NeurIPS).
Shen, Y. *, Cai, H. *, & Song, R. (2023). Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning. Journal of the American Statistical Association.
Zhang, W., Wu, T., Wang, Y., Cai, Y., & Cai, H.^ (2023) Towards Trustworthy Explanation: On Causal Rationalization. In International Conference on Machine Learning (ICML 2023).
Watson, RA. *, Cai, H. *, An, X., McLean, S., & Song, R. (2023). On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs. In International Conference on Machine Learning (ICML 2023).
Cai, H. *, Shi, C. *, Song, R., & Lu, W. (2023). Jump Q-Learning for Individualized Decision Making with Continuous Treatments. Journal of Machine Learning Research.
Cai, H.^, Lu, W., Marceau West R., Mehrotra DV., & Huang, L. (2022). CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search. Statistics in Medicine.
Cai, H. *, Shi, C *., Song, R., & Lu, W. (2021). Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings. Advances in Neural Information Processing Systems (NeurIPS).
Cai, H., Song, R., & Lu, W. (2021). ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning. International Conference on Learning Representations (ICLR).
Cai, H., Lu, W., & Song, R. (2020). On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies. International Conference on Machine Learning (ICML).
Wei, H. *, Cai, H. *, Shi, C., & Song, R. (2023+). On Efficient Inference of Causal Effects with Multiple Mediators. arXiv preprint arXiv:2401.05517.
Cai, H. *, Liu, S. *, & Song, R. (2023+). Is Knowledge All Large Language Models Needed for Causal Reasoning?. arXiv preprint arXiv:2401.00139.
Cai, H., Lu, W., & Song, R. (2023+). CODA: Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome. arXiv preprint arXiv:2104.10554.
Ma, T., Cai, H., Qi, Z., Shi, C., & Laber, E. B. (2023+). Sequential Knockoffs for Variable Selection in Reinforcement Learning. arXiv preprint arXiv:2303.14281.
Shen, Y., Wan, R., Cai, H., & Song, R. (2023+). Heterogeneous Synthetic Learner for Panel Data. arXiv preprint arXiv:2212.14580.
Price, K.+, Cai, H., Shen, W., & Hu, G. (2023+). How much does Home Field Advantage matter in Soccer Games? A causal inference approach for English Premier League analysis. arXiv preprint arXiv:2205.07193.
Current Students
Wenbo Zhang (Ph.D. Student)
Liner Xiang (Ph.D. Student)
Lijinghua Zhang (Ph.D. Student)
Zihang Xu (Undergraduate Student)
Louis Chu (Undergraduate Student)
Previous Students