Course Causal Inference with Directed Acyclic Graphs (DAGs)


Course leader: Riccardo Fusaroli

Language: English

Graduate school: Faculty of Arts

Course fee: 0.00 DKK

Status: Course is closed for applications

Semester: Spring 2024

Application deadline: 15/02/2024

Cancellation deadline: 15/02/2024

Course type: Blended learning

Start date: 04/03/2024

Administrator: Henriette Jaquet


All students are placed on a waiting list until we reach application deadline.

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 year. 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. 


  • Hünermund, P., Bareinboim, E. (2023). Causal Inference and Data Fusion in Econometrics. The Econometrics Journal.

Suggested Literature:

  • Chapters 2-3 – Pearl, J., Glymour, M., and Jewell, N. (2016). Causal Inference in Statistics – A Primer.
  • Chapters 3, 5, 7 – Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.
  • Cinelli, C., Forney, A., & Pearl, J. (2022). A Crash Course in Good and Bad Controls. Sociological Methods & Research.
  • Hünermund, P., Louw, B. (2022). On the Nuisance of Control Variables in Regression Analysis. Available on arXiv:
  • Knox, D., Lowe, W., Mummolo, J. (2020). Administrative Records Mask Racially Biased Policing. American Political Science Review, 114(3): 619–637.

Additional recommended literature:

  • Pearl, J., Mackenzie, D. (2018). Book of Why. Basic Books.
  • Pearl, J. (2009). Causality. Camdridge University Press, 2nd Edition.

Target group:

The ideal target group is phd students at any stage, with at least some experience in quantitative analysis of data using statistics, and in programming their own statistical analysis with R. 


Lectures and hands-on exercises.


Paul Hunermund - 


Campus Aarhus, TBA

Course dates:

  • 04 March 2024 12:00 - 16:00
  • 05 March 2024 09:00 - 16:00