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 one in-class final exam.

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.

Assessment consists of four assignments (80%) and one in-class final exam (20%).

Course Outline 01. Review of statistics; identification; the potential outcomes framework 02. The Furious Five – Randomized controlled trials 03. The Furious Five – Regression, Part I 04. The Furious Five – Regression (“Matchmaker”), Part II 05. The Furious Five – Instrumental variables 06. The Furious Five – Difference-in-differences 07. The Furious Five – Regression discontinuity design 08. Panel data models and synthetic control methods 09. A helicopter tour of causal machine learning in economics and political science 10. Modern high-dimensional econometrics 11. The double-lasso selection procedure 12. Decision trees and random forests 13. Industry Professional Guest Lectures; Speaker: Amazon Applied Scientist II, New York, United States (業界專業專題演講) 14. Causal forests 15. Heterogeneous treatment effects and policy learning 16. Review and discussion

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. 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.

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