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Directed Acyclic Graphs for Causal Inference - Online Course

A 4-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).

10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm

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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 June 4, we are offering this seminar as a 4-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.

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"I left the class eager to start applying these to the observational analyses that have been my bread and butter for nearly 20 years.” 

In a very welcoming and conversational style, Felix Elwert manages to gently shine a light on the sometimes-opaque field of causal inference. Starting with Rubin’s conditional ignorability, he introduces DAGs as the graphical definition of the data generating process and with excellent examples, connects them to modeling and clearly demonstrates the graphical criteria that allow for associations, both wanted and unwanted, as well as causal estimates. He succeeds greatly in meeting his goal of leaving students empowered to tackle causal inference on much surer footing. I left the class eager to start applying these to the observational analyses that have been my bread and butter for nearly 20 years. 

Terry Murphy

Penn State College of Medicine

"I particularly appreciated the strong qualitative explanations and emphasis on intuition that the teacher imparted..."

“I enjoyed the total journey, everything from basic concepts to complex topics. I particularly appreciated the strong qualitative explanations and emphasis on intuition that the teacher imparted in relation to DAGs, without endless nasty formal notation. This was ideal for maximum comprehension, especially coupled with the teacher’s gentle but persistent Socratic involvement of everyone in the class.” 

Mark Marshall

Te Whatu Ora, Health New Zealand

“Felix is an amazing instructor!"

“Felix is an amazing instructor! He did an outstanding job of making this really hard topic accessible.” 

Johannes Bauer

University of Erfurt 

"...I feel confident I can start using these methods.”

“I really like the simple explanations and the focus on questions. I’ve been brought to a point where I feel confident I can start using these methods.” 

Syed Shah

Medipeople

"The instructor knows the material quite well and is very enthusiastic.” 

“I appreciate that the instructor knows the material quite well and is very enthusiastic.” 

Victor Andreev

Arbor Research Collaborative for Health 

"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

"Can't recommend the course highly enough!"

Excellent course design and pedagogy—I couldn’t have asked for a better 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

"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