Forecasting Methods and Causal Inference for the Social Sciences
Course, Master's level, 2FK065
Expand the information below to show details on how to apply and entry requirements.
Spring 2027 Spring 2027, Uppsala, 100%, On-campus, English
- Location
- Uppsala
- Pace of study
- 100%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 25 March 2027–2 May 2027
- Language of instruction
- English
- Entry requirements
-
Fulfilment of the requirements for a Bachelor's degree, from an internationally recognised university. A quantitative research methods course at Master's level of at least 7.5 credits, or 60 credits of statistics at the undergraduate level, or equivalent. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
- Du som inte är medborgare i ett EU-/EES-land eller Schweiz måste i regel betala anmälnings- och studieavgift.
- First tuition fee instalment: SEK 14,250
- Total tuition fee: SEK 14,250
- Application deadline
- 15 October 2026
- Application code
- UU-70519
Admitted or on the waiting list?
Spring 2027 Spring 2027, Uppsala, 100%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 100%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 25 March 2027–2 May 2027
- Language of instruction
- English
- Entry requirements
-
Fulfilment of the requirements for a Bachelor's degree, from an internationally recognised university. A quantitative research methods course at Master's level of at least 7.5 credits, or 60 credits of statistics at the undergraduate level, or equivalent. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
About the course
The course deepens the knowledge of quantitative social science methodology that students have acquired during undergraduate studies. The aim is to develop your ability to use forecasting and causal inference methods to answer a research question and test theoretical arguments. The course offers training in how to design, estimate, and interpret common methods within forecasting and causal inference. Key techniques covered include Monte Carlo simulation, randomisation inference and prediction. There will be a focus on understanding the assumptions and goals of different methods and evaluating their strengths and weaknesses. To support your practical application, the course includes the use of statistical software.
Reading list
No reading list found.
Contact
- Ingalill Blad Ögren
- ingalill.blad-ogren@pcr.uu.se
- +46 18 471 23 49