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 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.

Course leader

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

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.

Course aim

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.


Prerequisites:
- Basic statistics
- Basic knowledge in R is helpful

Fee info

EUR 200: Student/PhD student rate.
The rates include the tuition fee and the course materials.
EUR 300: Academic/non-profit rate.
The rates include the tuition fee and the course materials.