DATA 8020: Advanced Causal Inference

Schedule: Fridays, 2:00 PM-4:50 PM, September 4-November 27, 2026
Venue: HKU IDS P603 Seminar Room, Graduate House
Instructor: Prof. Jin-Hong Du (jinhongd@hku.hk)
Teaching Assistant: Yingqi Fan (yingqi949@connect.hku.hk)
Offering unit: Institute of Data Science, The University of Hong Kong

Overview

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.

Topics

1. Causality in AI and data science
2. Foundations: causal languages and randomized experiments
3. Observational studies: identification and adjustment
4. Machine learning for causal inference: double machine learning and heterogeneous effects
5. Decisions, interference, and experiment design
6. Limits of identification: hidden confounding, latent structure, and foundation models

Learning Outcomes

Upon completion of this course, students will be able to:

1. Formulate research questions in AI and data science as well-defined causal problems.
2. Distinguish between causal identification, estimation, and inference.
3. Apply modern causal inference methods to real-world problems in biomedicine, genomics, the social sciences, and related areas.
4. Implement causal estimators in high-dimensional and computationally challenging settings using reproducible code.
5. Communicate causal assumptions, methods, results, and limitations effectively.

Prerequisites

Basic knowledge of probability, linear regression, and machine learning is expected. Programming experience in Python or R is required.

Project-Based Assessment

Assessment is organized around one causal-inference project, normally completed in pairs: project proposal (10%), group final presentation (30%), individual reflection (15%), final report and reproducibility material (30%), and an individual written AI methods portfolio (15%). Every student must use and critically assess generative AI during the project; access to a paid or advanced model is not required. Each group report must declare every member's individual contributions. See the project handout for the minimum deliverable requirements, AI-use rules, and candidate project directions.

HKU provides student AI resources, including chat interfaces and API endpoints, and students are encouraged to use them when suitable. Other providers are permitted with disclosure. Additional LLM support may be requested for a specific project need, but availability is not guaranteed.

Attendance

Attendance will be recorded at every class session. Students who meet the semester attendance requirement are eligible for a bonus, worth up to 10% of the final course grade and capped at 100%.

References

Primary textbook:

• Imbens, G. W., and Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.

Supplementary references:

• Hernan, M. A., and Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.
• Peters, J., Janzing, D., and Scholkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press.
• Ding, P. (2023). A First Course in Causal Inference. Chapman and Hall/CRC.
• Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., and Syrgkanis, V. (2024). Applied Causal Inference Powered by ML and AI. arXiv:2403.02467.

Additional public resources:

• Professor Linbo Wang's teaching page, including Directed Reading: Causal Inference.

Course Materials

Each major correction to the course materials earns one bonus point, up to a maximum of five bonus points.

HKU Semester 1 teaching begins on September 1, 2026 and ends on November 30, 2026. Lecture slides and project materials will be posted here as they become available.

Syllabus: PDF
Project: Handout

Final report, reproducibility material, and written AI methods portfolio due December 7, 2026 (tentative).