Causal Inference
Course, Master's level, 2ST124
Expand the information below to show details on how to apply and entry requirements.
Spring 2026 Spring 2026, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 24 March 2026–7 June 2026
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,500
- Total tuition fee: SEK 12,500
- Application deadline
- 15 October 2025
- Application code
- UU-76630
Admitted or on the waiting list?
- Registration period
- 18 December 2025–18 January 2026
- Information on registration from the department
Expand the information below to show details on how to apply and entry requirements.
Spring 2027 Spring 2027, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 25 March 2027–6 June 2027
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 15,750
- Total tuition fee: SEK 15,750
- Application deadline
- 15 October 2026
- Application code
- UU-76630
Admitted or on the waiting list?
- Registration period
- 25 February 2027–18 March 2027
- Information on registration from the department
About the course
The course utilises the potential outcome framework and the graphical, structural causal model framework for defining and estimating causal parameters for average causal effects and mediation analysis. Settings of randomised experiments and observational studies are covered. Underlying assumptions for defining causal parameters are described together with identifying assumptions for observational studies. Different causal effect estimators and their properties are studied and explored.