Excerpt from course description

Causality, Machine learning and Forecasting


Complete course description will be ready summer 2020.

We study two central problems in applied analytics: causal analysis on one side, and machine learning and forecasting techniques on the other. The aims of the two problems are complementary, and are here presented together to emphasise their differences and connections.

Course content

  • The problems surrounding analysing causality through observational data.
  • Experiments and quasi-experiments.
  • Fundamental principles of statistical learning and forecasting techniques: bias/variance trade-off, cross validation techniques and pseudo out of sample methods.
  • An overview of some central time series models and machine learning methods, and their connection to the above issues. Applied work will use Python.


This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.