Forecasting Methods and Causal Inference for the Social Sciences

7.5 credits

Course, Master's level, 2FK065

Expand the information below to show details on how to apply and entry requirements.

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

Läs mer om avgifter.

Application deadline
15 October 2026
Application code
UU-70519

Admitted or on the waiting list?

Information on registration from the department

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?

Information on registration from the department

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.

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