中文課名：經濟學與政治科學中的因果機器學習
授課教師：陳釗而
研究室：台大國際政經學院 忠興館401
Course Description
This course introduces machine learning methods and their applications to causal inference in economics and political science. In data-rich empirical research, the central challenge lies not only in asking meaningful questions but also in applying empirical methods with rigor and good judgment. To this end, we examine examples of off-the-shelf machine learning tools used in economics and political science, followed by highlights from the emerging econometric literature that integrates machine learning with causal inference.
Students are expected to complete all problem sets and assigned readings. Active participation is strongly encouraged—questions and class discussion will be an integral part of the learning process. Mastery of the techniques taught in this course will be evaluated through four assignments and a term paper.
Course Objective
By the end of the course, students will have developed a working familiarity with causal machine learning techniques as well as practical skills in data handling and programming.
Course Outline
- Regression [slides]
- Potential outcomes and RCT
- Selection on observables
- Selection on unobservables – LATE
- Difference-in-differences
- Regression discontinuity design
- Intro to causal machine learning
- Double robustness
- High-dimensional econometrics
- Double selection procedure and Orthogonality
- Double machine learning
- Meta learners
- Causal forests
- Policy learning
- Frontier
- Review and discussion
Readings
- Chen, Jau-er, and Jing, Annette (2025). “Recent Advances in Causal Machine Learning and Dynamic Policy Learning.” Wiley Interdisciplinary Reviews: Computational Statistics, 17(4), 1–27.
- Chernozhukov, V. & Hansen, C. & Kallus, N. & Spindler, M. & Syrgkanis, V. (2024): Applied Causal Inference Powered by ML and AI. CausalML-book.org
- Angrist, J., and Pischke, J. (2009). Mostly Harmless Econometrics. Princeton University Press.
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R, 2nd ed. Springer.
- 2025 經濟計量實證研習營（機器學習與因果推論）– 臺灣經濟計量學會主辦
- Econometric Theory textbook: Chapters 18, 19, 20, 22 and 23.
Grading
Assessment consists of four assignments (60%) and a term paper accompanied by a 15–20 minute recorded presentation explaining the term paper (40%).
Jauer 