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Curricular information is subject to change
By the end of this course, students should be able to:
- develop credible identification strategies
- critically evaluate methods of causal inference
- apply methods of causal inference to answer economic questions
The course will cover the following:
Basics of causality
Directed acyclical graphs & potential outcomes
Difference-in-Difference estimation
Instrumental variables
Marginal treatment effects
Regression discontinuity and kink designs
Synthetic controls
Bounding
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Autonomous Student Learning | 176 |
Total | 200 |
Requires prior knowledge of advanced econometrics. Students should have taken PhD Econometrics 1.
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Assoc Professor Stefanie Haller | Lecturer / Co-Lecturer |