• Oct to Dec 2023: I will present my works at INFORMS Annual Meeting, CMStatistics, ICSDS.

• 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 and collaborators!

• 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.

• The Information and Computer Sciences (ICS) Research Award, University of California Irvine, 2023
• Academic Senate Council on Research, Computing and Libraries (CORCL) Award, University of California Irvine, 2023
• Amazon Web Services Cloud Research Award, Amazon, 2022
• ENAR Distinguished Student Paper Award, The International Biometric Society, 2020
• Nominated for the Outstanding TA Award, NCSU, 2019 and 2021
• Mu Sigma Rho, National Statistics Honor Society, NCSU, 2017 - Present
• National Undergraduate Research Fund, 2017
• Meritorious Winner in American Mathematical Contest, 2016

(* co-first author, ___ graduate student)

• Cai, H., Wang, Y., Jordan, M., & Song, R. (2023). On Learning Necessary and Sufficient Causal Graphs. Advances in Neural Information Processing Systems (NeurIPS).

  • Spotlight Paper.
  • Abstract       Paper

• Shen, Y. *, Cai, H. *, & Song, R. (2023). Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning. Journal of the American Statistical Association.

  • IMS 2022 Hannan Graduate Student Travel Award.
  • Abstract       Paper       Slides

• 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).

  • Abstract       Paper      

• 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).

  • JSM 2023 Mental Health Statistics Section Student Paper Award.
  • Abstract       Paper

• Cai, H. *, Shi, C. *, Song, R., & Lu, W. (2023). Jump Q-Learning for Individualized Decision Making with Continuous Treatments. Journal of Machine Learning Research.

  • Abstract       Paper       CRAN       Slides

• Cai, H., Lu, W., Marceau West R., Mehrotra DV., & Huang, L. (2022). CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search. Statistics in Medicine.

  • Abstract       Paper       GitHub       Slides

• 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).

  • ENAR 2021 Distinguished Student Paper Awards.
  • Abstract       Paper       GitHub       Slides

• 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).

  • Abstract       Paper       GitHub       Slides

• 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).

  • Abstract       Paper       Code       Slides

Some Preprints

• Cai, H., Lu, W., & Song, R. (2023+). CODA: Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome. arXiv preprint arXiv:2104.10554.

  • Abstract       Paper       GitHub       Slides

• 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.

  • Abstract       Paper

• Shen, Y., Wan, R., Cai, H., & Song, R. (2023+). Heterogeneous Synthetic Learner for Panel Data. arXiv preprint arXiv:2212.14580.

  • Abstract       Paper

• Price, K.+, Cai, H., Shen, W., & Hu, G. (2022+). How much does Home Field Advantage matter in Soccer Games? A causal inference approach for English Premier League analysis. arXiv preprint arXiv:2205.07193.

  • Abstract       Paper

• Wenbo Zhang (Ph.D. Student)
• Shih Ting Huang (Master Student)
• Louis Chu (Undergraduate Student)
• Guanchen Wu (Undergraduate Student)
• Miya Wang (Undergraduate Student)
• Wang Ma (Undergraduate Student)

University of California Irvine (2022-)

Instructor:

• STATS 120C/281C: Introduction to Probability and Statistics III (Spring 2023, two sections)

North Carolina State University (Prior to 2022)

Lab Instructor:

• ST 703: Statistical Methods I (Fall 2019)
• ST 114: Introduction to Statistical Programming - Python (Spring 2022)

Teaching Fellow:

• ST 422: Introduction to Mathematical Statistics II (Fall 2021)
• ST 745: Analysis of Survival Data (Spring 2021)
• ST 405/505: Applied Nonparametric Statistics (Fall 2020)
• ST 311: Introduction to Statistics (Spring 2019)
• ST 790: Financial Statistics (Fall 2018)
• ST 312: Introduction to Statistics II (Spring 2018)
• ST 511: Introduction to Statistics for Biological Sciences (Fall 2018)
• ST 350: Economic and Business Statistics (Fall 2017)