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Directed Acyclic Graphs for Causal Inference - Online Course

A 3-Day Livestream Seminar Taught by

Felix Elwert
Course Dates:

Wednesday, January 18 –
Friday, January 20, 2023

Schedule: All sessions are held live via Zoom. All times are ET (New York time).

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

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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 January 18, 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.

More details about the course content

Computing

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"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

"The lectures were very well organized with lots of examples.” 

“The depth of the instructor’s knowledge and experience in the field is great. The lectures were very well organized with lots of examples.” 

SeungYong Han

Arizona State University 

“I love everything about the course, and enjoyed every minute of the lectures..."

“I love everything about the course, and enjoyed every minute of the lectures—Felix is such a great instructor! I appreciate Statistical Horizons’ effort and will recommend the course to relevant people I know. Thank you, Felix! Thank you, Statistical Horizons!”

X. San Liang

Fudan University

"Dr. Elwert's slides are well-developed. His presentation is engaging."

“The content is relevant for me personally. Dr. Elwert’s slides are well-developed. His presentation is engaging. And he’s also very respectful of the students. I was really impressed.” 

Pat Zimmerman

Medtronic 

“This class is very interactive with many thought-provoking examples..."

“This class is very interactive with many thought-provoking examples sampled from diverse disciplines. Participants asked terrific questions. The material presented is important and timely. I came away with a number of insights I will now apply to my own research. A number of us have exchanged contact information and will follow each other’s progress on this topic.”

Bill Burns

California State University, San Marcos

“The speaker was very knowledgeable and was able to present concepts in an easy to digest manner..."

“The speaker was very knowledgeable and was able to present concepts in an easy to digest manner, utilizing a set of demonstrative real-life examples. The availability of the class notes beforehand and the Zoom recordings following each session was very helpful, particularly as I was not able to attend the sessions in real-time.”

Trang Pham

The University of Queensland, Child Health Research Centre

“Highly informative, well balanced in terms of the theory..."

“Highly informative, well balanced in terms of the theory and applied aspects of developing non-parametric causal DAGs and causal inference. Very well taught by an expert who is humble, approachable and very passionate about their field.”

Paul Agius

Burnet Institut

“This course is rigorous but very applied..."

“This course is rigorous but very applied. I had already read about this topic but the examples really communicated how to use DAGs.”

John Antel

University of Houston