This course will offer an introduction into causal inference with directed acyclic graphs (DAGs). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal modeling. Originally developed in the computer science and artificial intelligence field, they have 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 will provide 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 will lie 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 you through the presented material. There are no prerequisites for participating, but a good working knowledge of basic statistics and R is a plus.

 

Organizational structure of the course

This is a three-day course with a total amount of 12 hours of virtual class time. The course will be organized in two two-hour sessions (with short breaks in between) per day. The afternoon sessions will comprise practice tutorials in R on the topics covered in the lecture sessions. Tutorials will be led by a teaching assistant; while the lecturer will be available for questions.

You will find the course useful if:

By the end of the course, you will:

Software and hardware requirements

You will need a personal or laptop computer to successfully participate in this course.

 

Practical exercises will be taught in R. To follow the hands-on examples, you will need to have R (https://cran.r-project.org/) and RStudio (https://posit.co/products/open-source/rstudio/) installed on your device before the course starts. Both programs are free and open source.

 

For an introduction or refresher in R programming, you might consider enrolling in GESIS's two-day onsite course, “Introduction to R for Data Analysis” held the week before this course within the Summer School in Cologne, or the four-day online workshop, “Introduction to R” offered in May.