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, matching method, instrumental variables, differences-in-differences method, synthetic controls method, regression discontinuity (kink) design, machine learning method, text mining, and GIS data. We will especially focus on the practical implementation of these methods and tips for data management by a writing term paper. Students need to use Latex to type their term paper and upload theircodes to GitHub for replication. After taking this course, students should be able to conductempirical research independently and know how to write a scientific paper.
能力項目說明
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
Please see course website: https://causaldatalab.wordpress.com/2023/02/01/causal-inference-and-data-science-in-economics-spring-2023/
Grading Policy
1. Research questions presentation in office hour (10%)
2. Three empirical homework (30%)
3. Research progress presentation (10%)
4. Term paper presentation (10%)
5. Term paper (40%): milestones throughout the term
https://causaldatalab.wordpress.com/