Students should be conversant about methods for estimating and identifying causal effects, and have the intuition and skills necessary to design and implement empirical strategies for causal analysis.
Students should be able to
- Understand causal modeling
- Gain the statistical tools for causal analysis
- Design and implement empirical strategies for causal analysis
- Become aware of underlying assumptions, strengths, and weaknesses of different experimental and quasi experimental approaches
- Become acquainted with key players in the development and implementation of strategies for causal analysis
The student should be conversant in the world of causal analyses, using instrumental variables, difference-in-difference, regression discontinuity design, synthetic control methods, and randomized controlled trials. They should be able to be consumers and producers of causal analyses. They should have the background to develop and implement ethically sound and scientifically valid empirical strategies. They should also have proficiency in software designed to implement causal analysis.
Required prerequisite knowledge
Recommended previous knowledge
|Individual essay||1/1||Pass - Fail|
- Full participation during the whole course is required. (One week on intensive lectures and PC-lab exercises).
- Active participation in discussions in general.
- In addition, there is a term paper requirement ( 8-10 pages, demonstrating knowledge and application skills).
Method of work
Estimated student workload in hours:
1. Lecture 28
2. Seminar/lab exercise 8
3. Specific supervision 20
4. Student's self studies 50
5. Written assignment 40
Sist oppdatert: 05.07.2020