GESIS Training Courses

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Short Course D: Causal Inference with Directed Acyclic Graphs (DAGs)

Online via Zoom
Course duration
09:30-15:00 CEST
General Topics:
Course Level:
Summer School
Software used:
Students: 200 €
Academics: 300 €
Commercial: 600 €
Additional links
Lecturer(s): Paul Hünermund

About the lecturer - Paul Hünermund

Course description

This course offers an introduction into causal inference with directed acyclic graphs (DAGs). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach for causal modeling. Originally developed in the computer science and artificial intelligence field, they recently gained increasing traction also in other scientific disciplines (such as economics, political science, sociology, health sciences, and philosophy). DAGs allow to check the validity of causal statements based on intuitive graphical criteria, that do not require algebra. In addition, they open the possibility to completely automatize the causal inference task with the help of special identification algorithms. As an encompassing framework for causal reasoning, DAGs are becoming an essential tool for everyone interested in data science and machine learning. The course provides a good overview of the theoretical advances that have been made in the field of causal data science in the last thirty years. The focus lies on practical applications of the theory and students will be put into the position to apply the covered methodologies in their own research. In particular, common causal inference challenges such as backdoor adjustment, bad controls, instrumental variables, selection bias, and external validity will be discussed in one single framework. Hands-on examples using dedicated libraries in R will guide through the presented material. There are no prerequisites for participating, but a good working knowledge in basic statistics and R are a plus.
A detailed syllabus with course times and literature will soon be available for download here.

Target group

Participants will find the course useful if:
  • they plan to do quantitative analyses in their own research.
  • they want to get a better conceptual understanding of causal inference.
  • they are curious to learn new data science skills.
  • they are interested in an introduction into the field of causal AI.

  • Learning objectives

    By the end of the course participants will:
  • gain a better understanding of common causal inference problems.
  • be able to draw better connections between a variety of quantitative methodologies.
  • master a powerful formalism for causal modeling.
  • have deeper insights into methodological approaches from the field of causal AI.
  • acquire various practical tools for solving causal inference challenges in their own research.
    Organizational structure of the course
    The class is organized in two times two hours sessions (with short breaks in between) per day. The afternoon sessions will comprise practice tutorials in R run by a TA (the lecturer will be available for questions).


  • Basic statistics
  • Basic knowledge in R is helpful
    Software and hardware requirements
    Practice exercises will be taught in R. To follow the hands-on examples, it is recommended that participants have R ( and RStudio ( installed on their laptops. Both programs are free and open source.