Skip to content

Directed Acyclic Graphs for Causal Inference - Online Course

A 3-Day Livestream Seminar Taught by

Felix Elwert
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:00am-12:30pm (convert to your local time)

Watch Sample Video

This seminar offers an applied introduction to directed acyclic graphs (DAGs) for causal inference. DAGs are a powerful new tool for understanding and resolving causal issues in empirical research. DAGs are useful for social and biomedical researchers, and for business and policy analysts who want to draw causal inferences from non-experimental data. A major attraction of DAGs is that they are “algebra-free,” relying instead on intuitive yet rigorous graphical rules.

Starting September 21, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.

Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

More details about the course content


Who should register?

Seminar outline

Payment information

"I felt like a kid on Christmas morning opening a new toy.”

“Felix offered clear explanations of concepts and theory throughout the course, and had enough applied examples and exercises that I could create and use at least simple DAGs by the end of the course. I thought Felix did a good job explaining some of the causal concepts efficiently and clearly, and he gave some examples and phrasing that I may be borrowing the next time I’m working with someone without expertise in causal inference. I’ve learned that as a tool, DAGs are a straight-forward way to see and keep track of the assumptions we’re making in causal models, to help identify testable causal paths, and to communicate graphically. All of which can be very helpful in my role. I felt like a kid on Christmas morning opening a new toy.”

Richard Swartz

Rice University

"The speaker was fantastic in terms of knowledge and didactics.”

“The topics were well-organized and paced; the speaker was fantastic in terms of knowledge and didactics.”

Michael Grabner

HealthCore, Inc.

"Felix is brilliant as a lecturer.”

“I liked that this course was very much straight-to-the point, and the concepts were explained rather than digging into heavy math. Felix is brilliant as a lecturer.”

Aliaksei Laureshyn

Lund University

"Can't recommend the course highly enough!"

Excellent course design and pedagogy—I couldn’t have asked for a better 3-day course on DAGs and causal inference. Dr. Elwert is truly a great teacher and made the course materials very accessible. This would be an excellent introductory course for social scientists interested in causal inference. Can’t recommend the course highly enough! 

Sae Hwang Han

University of Texas at Austin