Not recorded
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
1) Basics of research design
2) Randomised experiments
3) Instrumental Variables
4) Regression Discontinuity
5) Difference-in-differences
6) Synthetic control
About this Module
Student Effort Hours:
| Student Effort Type | Hours |
|---|---|
Not yet recorded. |
|
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
Not yet recorded. |
|||||
Carry forward of passed components
Not yet recorded
Not yet recorded
| Remediation Type | Remediation Timing |
|---|---|
Not yet recorded |
|
Not yet recorded