Causal inference

This course will be run on-line via zoom, except for the final exam which is onsite (but local examination might be arranged for PhD students at other universities).

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.


More information and application link here:

Course starts in November 2020

Course Data
Type of schedule: 
Travel friendly schedule
Credits (ECTS):