Forecasting Methods and Causal Inference for the Social Sciences

7.5 credits

Syllabus, Master's level, 2FK065

A revised version of the syllabus is available.
Education cycle
Second cycle
Main field(s) of study and in-depth level
Peace and Conflict Studies A1N
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Department Board, 27 October 2022
Responsible department
Department of Peace and Conflict Research

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.

Learning outcomes

After completion, the students are expected to:

  • be familiar with the different goals, strengths, and weaknesses of causal inference- and forecasting-oriented research methodologies
  • know how to specify, interpret, and evaluate forecasting models
  • be familiar with tools and concepts commonly used in forecasting
  • be able to design, estimate, and interpret causal inference strategies
  • be able to articulate, discuss, evaluate, and problematize assumptions of causal inference strategies
  • have attained comprehensive knowledge of the R statistical software package
  • have attained knowledge about how to draw inferences from forecasting models
  • independently write assignments within a given time frame


The course will provide the students with the ability to specify, estimate, and interpret common methods within forecasting and causal inference. In addition, there will be a focus on evaluating different methods and highlighting their strengths and weaknesses. During the course the students will work with a variety of practical applications. The teaching will primarily rely on the R programming language and will also introduce basic programming techniques required for efficient and transparent research procedures. In addition, the course may include lectures on special topics related to forecasting or causal inference.


Instruction will be through a mix of lectures and seminars. The language of instruction is English.   


Assessment will be based on written assignments and active participation in class. All assignments must be handed in.

Grades: Pass with distinction (VG), Pass (G), Fail (U).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.

No reading list found.