DATA8020 - Advanced Causal Inference
Course Instructor
Professor Jinhong DU
Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Statistics and Actuarial Science, School of Computing and Data Science
Professor Jinhong Du is an HKU-100 Assistant Professor at the University of Hong Kong, beginning in Fall 2025. He holds joint appointments in the HKU Musketeers Foundation Institute of Data Science, and the Department of Statistics and Actuarial Science, School of Computing and Data Science.
His research bridges statistical theory and high-impact applications, focusing on causal inference, interpretable machine learning, high-dimensional statistics, and statistical genomics. Dr. Du’s work has been published in leading venues across multiple disciplines, including premier statistics journals like the Journal of the American Statistical Association and the Journal of the Royal Statistical Society, Series B, top machine learning conferences like NeurIPS and ICML, and prominent scientific journals such as the Proceedings of the National Academy of Sciences.
He earned his Ph.D. in Statistics and Machine Learning from Carnegie Mellon University, an M.S. in Statistics from the University of Chicago, and a B.S. in Statistics from Sun Yat-sen University.
Course Description
This course introduces modern causal inference from statistical, machine learning, and computational perspectives. Students will learn how to formulate causal questions, identify causal effects under explicit assumptions, and develop statistically principled and computationally scalable methods for estimation and inference from observational and experimental data. The course combines classical statistical foundations with modern machine learning tools, including double machine learning, high-dimensional modeling, and heterogeneous treatment effect estimation, to prepare students for research in causal methodology and data-rich scientific applications.
Prerequisites
Basic knowledge of probability, linear regression, and machine learning, and programming in Python or R will be required. Tutorials on probability, semiparametric inference, regression and optimization related to the course will be offered.