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Causal inference

  • Number of credits 7.5 credits
  • Level Master’s level
  • Starting Autumn Term 2023

About the course

Causal inference is the goal of many empirical studies in the health and social sciences. In a causal analysis, a causal parameter is formally defined and the underlying assumptions are explicitly stated. These assumptions clarifies the differences between experimental data and observational data for making causal conclusions. In the course we focus on two dominating approaches for causal analyses, i) the potential outcomes framework and ii) structural analyses (graphical models). We learn the foundations for these two frameworks as well as methods for estimation of causal effects in experimental and observational studies. Data-examples are given throughout the course and practical problem solving and data analyses are carried out in parallel with the theoretical material.

Application and eligibility

Causal inference, 7.5 credits

Det finns inga tidigare terminer för kursen Autumn Term 2023 Det finns inga senare terminer för kursen

The information below is only for exchange students

Starts

31 October 2023

Ends

14 January 2024

Study location

Umeå

Language

English

Type of studies

Daytime, 50%

Required Knowledge

Univ: 90 credits (hp) in statistics and/or mathematical statistics, or equivalent. Proficiency in English equivalent to Swedish upper secondary course English B/6
 

Selection

Students applying for courses within a double degree exchange agreement, within the departments own agreements will be given first priority. Then will - in turn - candidates within the departments own agreements, faculty agreements, central exchange agreements and other departmental agreements be selected.

Application code

UMU-A2782

Application

This application round is only intended for nominated exchange students. Information about deadlines can be found in the e-mail instruction that nominated students receive. The application period is closed.

Contact us

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Contactpersons for the course are:
Study advisor, Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University