Causal Inference with Directed Acyclic Graphs (DAGs)

online course

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.

Course leader

Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School.

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.

Course aim

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.

Prerequisites:
- Basic statistics
- Basic knowledge in R

Fee info

EUR 220: Student/PhD student rate.
The rates include the tuition fee and the course materials.
EUR 330: Academic/non-profit rate.
The rates include the tuition fee and the course materials.