Type of Credit: Elective
Credit(s)
Number of Students
This course will survey empirical methods for conducting causal inference and data science in economics. We will focus on recent advances in these methods as well as their empirical applications. The topics will include randomized (field) experiments, machine learning, matching method, differences-in-differences method, synthetic controls method, regression discontinuity (kink) design, instrumental variables, and GIS data. We will especially focus on the practical implementation of these methods and tips for data management by a writing term paper. After taking this course, students should be able to conduct empirical research independently.
能力項目說明
Please see course website: https://causaldatalab.wordpress.com/2021/02/21/causal-inference-and-data-science-in-economics-spring-2021/
Grading Policy
1. Two compulsory office hour (10%)
2. Three empirical/writing homework (30%)
3. Research progress presentation (10%)
4. Term paper presentation (10%)
5. Term paper (40%): milestones throughout the term
6. You will get extra 5 points in your final grade if you use Latex to type your paper
https://causaldatalab.wordpress.com/