This course will bring students to the cutting edge in causal inference using machine learning, giving them a solid theoretical understanding and ready-to-deploy tools for research. Using machine learning for estimation and inference of treatment effects has become an important part of modern academic economics. Students will learn to implement deep learning and conduct semiparametric inference using double machine learning. Students in this class will learn the theoretical underpinnings of this material as well as howto carefully and correctly apply the techniques in research. The course will prepare students for both theoretical and applied dissertation research.

Prerequisites: Second year Ph.D. in Economics graduate student standing.

4

Units

Letter

Grading

1, 2, 3

Passtime

Graduate students only

Level Limit

Letters and science

College
These majors only econ esm
FARRELL M
No info found
Spring 2024 . T B A
NH 2212
T R
14:00 PM - 15:15 PM
ECON 245N Farrell M Spring 2024 Total: 14
ECON 230C
2 / 15 Enrolled
Individual Taxation
Alisa Tazhitdinova 2.5
M W
14:00 PM - 15:15 PM
100.0% A
ECON 230I
4 / 15 Enrolled
Health Economics
Ted Frech 3.1
T R
17:00 PM - 18:15 PM
100.0% A
ECON 230D
2 / 15 Enrolled
Capital Taxation
Alisa Tazhitdinova 2.5
M W
14:00 PM - 15:15 PM
100.0% A
ECON 241A
6 / 14 Enrolled
Econometrics
Farrell M
M W
08:00 AM - 09:15 AM
61.2% A
ECON 250D
5 / 15 Enrolled
Population Economics
Lundberg S J
M W
17:00 PM - 18:15 PM
97.0% A
ECON 260E
5 / 15 Enrolled
Natural Resource Economics: Continuous-Time Methods
Plantinga A
T R
14:00 PM - 15:15 PM
100.0% A