Learning Outcomes:

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

Indicative Module Content:

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

Learning Requirements:

Requires prior knowledge of advanced econometrics. Students should have taken PhD Econometrics 1.

Additional Information:
It is recommended that students have taken PhD Econometrics I before taking this course. However, this is not strictly necessary. The minimum requirement is M.Sc-level econometrics and/or statistics. When in doubt, contact the module coordinator.


 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Continuous Assessment: Problem sets and other assignments	Throughout the Trimester	n/a	Graded	No	50
Examination: Final exam	2 hour End of Trimester Exam	No	Graded	No	50

 

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

Spring
Lecture	Offering 1	Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33	Thurs 14:00 - 15:50