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.

You will find the course useful if:

By the end of the course you will:

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).

 

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 (https://cran.r-project.org/) and RStudio (https://posit.co/download/rstudio-desktop/) installed on your device before the course starts. Both programs are free and open source.