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

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Jacqueline Schüller
Tel: +49 0221 47694-160

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

Online via Zoom
Course Duration
Wed-Fr: 9:30-11:30 | 13:00-15:00 CEST
General Topics:
Course Level:
Software used:
Students: 220 €
Academics: 330 €
Commercial: 660 €
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 us 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 year. The focus lies on practical applications of the theory, and you will be put into the position to apply the covered methodologies in your 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 is a plus.
The full syllabus of the course including the day-to-day schedule will be published here in April.

Target group

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

Learning objectives

By the end of the course you 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 your own research.
Organizational structure of the course
The class is organized in two two-hour sessions (with short breaks in between) per day. The afternoon sessions will comprise practice tutorials in R run by a teaching assistant (the lecturer will be available for questions).


  • Basic statistics
  • Basic knowledge in R
Software and hardware requirements
You will need a personal or laptop computer to successfully participate in this course.
Practice exercises will be taught in R. To follow the hands-on examples, you have to have R ( and RStudio ( installed on your device before the course starts. Both programs are free and open source.

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