# ECON42720 Causal Inference & Policy Evaluation

Most empirical research --- be it in academia, policy advice or industry --- asks causal questions. Does a higher minimum wage increase unemployment? Does a new product line generate higher revenues? Do higher interest rates reduce inflation? Etc etc. Answering such questions is challenging because in most cases we cannot run experiments. In the last 30 years, statisticians have developed a toolbox named "Causal Inference" that allows researchers to quantify causal effects without experiments. This course will introduce students to the most important techniques in causal inference. These are canonical research designs that can be applied to many interesting questions:

1) Basics of research design
2) Randomised experiments
3) Instrumental Variables
4) Regression Discontinuity
5) Difference-in-differences
6) Synthetic control

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Curricular information is subject to change

Learning Outcomes:

On completion of the module, students will be able to:

- Develop a research design for a given causal question at hand
- Assess the suitability canonical research designs for a given research question
- Apply the research designs using R
- Effectively communicate their findings

Indicative Module Content:

1) Basics of research design
2) Randomised experiments
3) Instrumental Variables
4) Regression Discontinuity
5) Difference-in-differences
6) Synthetic control

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

100

Lectures

24

Total

124

Approaches to Teaching and Learning:
Lectures, Computer Labs, Group Projects
Requirements, Exclusions and Recommendations
Learning Recommendations:

ECON41820 Econometrics or a comparable course is a prerequisite.

Module Requisites and Incompatibles
Not applicable to this module.

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

60

Assignment: Two empirical assignments, 20% each Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

40

Carry forward of passed components
No

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Scott Cunningham (2021), Causal Inference -- The Mixtape, Yale University Press
Nick Huntington-Klein (2022), The Effect, Routledge
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

Spring

Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33 Thurs 09:00 - 10:50