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Machine Learning for Estimating Causal Effects - Online Course

A 3-Day Livestream Seminar Taught by

Ashley Naimi
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) Thursday-Saturday
1:30pm-4:00pm Thursday, 1:30pm-3:30pm Friday & Saturday

Machine learning is increasingly being used to evaluate cause-effect relations with social, economic, health, and business data. When used properly, these tools have tremendous potential to yield robust effect estimates with minimal assumptions. However, both machine learning and causal inference techniques add considerable complexity to an analysis, making proper use a challenge.

In this seminar, you will learn how to minimize biases that result from improper use of machine learning methods to answer practical questions about cause-effect relations in non-experimental data.

Starting October 6, 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.

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Computing

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"...I would rank Dr. Naimi the BEST instructor of all."

“I really liked how the instructor covered concepts and then went overs the codes. I have taken more than a dozen workshops from Statistical Horizons during last 5-6 years and I would rank Dr. Naimi the BEST instructor of all. He gave detailed notes and codes and adequately explained everything. He responded well to all queries from the participants.”

Towhid Islam

University of Guelph

“Professor Naimi did an excellent job..."

“Professor Naimi did an excellent job, combining stats rationale and applications together in his teaching. He addressed students’ questions so well and has created a welcoming environment in which everyone feels comfortable asking questions.”

Lin Xiu

University of Minnesota, Duluth

“Professor Naimi has a very clear understanding and presentation of the philosophical issues...”

“Professor Naimi has a very clear understanding and presentation of the philosophical issues involved in causal inference.”

Georges Monette

York University

"The instructor’s PDFs with both the theoretical and applied language + R code are invaluable."

“The instructor’s PDFs with both the theoretical and applied language + R code are invaluable. Those materials are among the best I have ever seen from one of these courses. I will definitely be referencing them in the future.”

John Merranko

University of Pittsburgh Medical Center

"The course had a great balance of introductions, intuitions, theory, and practical implementation!”

“Ashley was a fantastic teacher and his handling of questions and discussion was excellent. The course had a great balance of introductions, intuitions, theory, and practical implementation!”

James Davis

Trinity College Dublin