Causal Inference

The course is given online and starts November 1, contact Xavier de Luna if you are interested (xavier.de.luna@umu.se). Course details: https://www.canvas.umu.se/courses/15175/assignments/syllabus 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.
Course Data
Type of schedule: 
Travel friendly schedule
University: 
Umeå
Level: 
Master
PhD
Credits (ECTS): 
7.50
Offered: 
2024:2