Hengrui Cai (蔡亨瑞)

Hengrui Cai (蔡亨瑞)

Assistant Professor

University of California Irvine

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

  • Causal Inference and Causal Structure Learning
  • Reinforcement Learning and Bandits
  • Natural Language Processing and Explainable Deep Learning
  • Precision Medicine
  • PhD in Statistics, 2022

    North Carolina State University

  • B.S. in Statistics, 2017

    Zhejiang University


Awards and Honors

  • The Information and Computer Sciences (ICS) Research Award, University of California Irvine, 2023 & 2024
  • 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

Selected Publications

(* co-first author, ___ graduate student author, ^ corresponding author)

Some Preprints


Current Students

  • Wenbo Zhang (Ph.D. Student)

  • Liner Xiang (Ph.D. Student)

  • Lijinghua Zhang (Ph.D. Student)

  • Zihang Xu (Undergraduate Student)

Previous Students

  • Shih Ting Huang (Master Student, Graduated at Summer 2023)
  • Guanchen Wu (Undergraduate Student, Graduated at Winter 2024)
  • Louis Chu (Undergraduate Student, Graduated at Spring 2024)


University of California Irvine (2022-)


  • STATS 295: Causal Machine Learning (Fall 2024, elective course)
  • STATS 120C/281C: Introduction to Probability and Statistics III (Spring 2024, two sections)
  • STATS 120C/281C: Introduction to Probability and Statistics III (Spring 2023, two sections)
  • STATS 280: Seminar in Statistics (Fall 2023)

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)


  • Causal Decision Making: This webpage focuses on integrating causal decision-making into a unified framework, including causal structure learning (CSL), causal effect learning (CEL), and causal policy learning (CPL)! Available on https://causaldm.github.io/Causal-Decision-Making/.

  • Sepsis - EHR Benchmark Environment for RL: This repository is an online reinforcement learning (RL) environment that allows its users to benchmark different RL algorithms in treating sepsis patients given their electronic health record (EHR) data, built by Guanchen Wu as his learning outcome with me in STATS 199 Individual Study at University of California, Irvine. Available on https://github.com/wgc369/Sepsis---EHR-Benchmark-Environment-for-RL.

  • DJL: Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings. Available on GitHub.

  • ANOCE-CVAE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning. Available on GitHub.

  • CODA: Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome. Available on GitHub.

  • CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search. Available on GitHub.

  • JQL: Jump Q-Learning for Individualized Interval-Valued Dose Rule. Available on CRAN.

  • APtool: Average Positive Predictive Values (AP) for Binary Outcomes and Censored Event Times. Available on CRAN.

  • APRL: Assets Portfolio Model by Reinforcement Learning. Available on GitHub.