Departments' graduate courses
Course start and periodicity may vary. Please see details for each course for up-to-date information. The courses are managed and administered by the respective departments. For more information about the courses, how to sign up, and other practical issues, please contact the examiner or course contact to be found in the course information.
Causality & Causal Inference
- Course code: FDAT131
- Course higher education credits: 7.5
- Department: COMPUTER SCIENCE AND ENGINEERING
- Graduate school: Computer Science and Engineering
- Course start: 2020-09-01
- Course end: 2020-10-30
- Course is normally given: LP1
- Language: The course will be given in English
- Nordic Five Tech (N5T): This course is free for PhD students from N5T universities
The content is structured into 8 modules, one for each week of the course. We begin by briefly covering necessary prerequisites, such as probabilistic graphical models, and by introducing structural definitions of causality. This will enable us to study sufficient conditions for inferring causal relationships between random variables. In the second half of the course, we study estimation of causal effects and policy evaluation. These are important topics in, e.g., epidemiology. At the end of the course, each student will present a research paper on a topic of their choice.
The course will be based on public material such as research papers and ebooks available online. There will be weekly in-class or video lectures given by Fredrik. If you are interested in the topic and would like to read a book on the subject, consider one of the following:
- Pearl, Judea. Causality. Cambridge university press, 2009.
- Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
- Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015. 2nd Edition.