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Machine Learning for Estimating Causal Effects - Online Course

A 4-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).

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

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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 August 6, 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. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

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Computing

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"I enjoyed the focus on the application of the techniques in this course."

“I enjoyed the content, teacher’s knowledge of the subject, and the focus on the application of the techniques in this course. I want to thank Dr. Naimi for the course. I learned a ton of stuff and I’m very motivated to continue studying the subject. 

Carlos Quintanilla

Incae Business School

"It was very hands on with R examples and simulations..."

“This course was good for non-technical folks who want to understand why machine learning can be useful in causal inference. It was very hands on with R examples and simulations rather than algebra.” 

Alessandro Cozzi-Lepri

UCL

“This was an awesome course!"

“This was an awesome course! It was absolutely worth getting up for the 4am start time (from New Zealand). Ashley was absolutely fantastic. He was knowledgeable, articulate, patient, thoughtful, and friendly. His students (in the University setting or in these mini-courses) are lucky to have him.”

Jeff Rothschild

High Performance Sport New Zealand

"...Professor Naimi focused his explanation on the methods that have proven to be the most important..."

“I liked that Professor Naimi focused his explanation on the methods that have proven to be the most important and useful for most researchers. I also liked that questions were answered even though they might have veered off the general purpose of the workshop.”

Pamela Fernainy

University of Montreal

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

“I really liked how the instructor covered concepts and then went over the codes. I have taken more than a dozen workshops from Statistical Horizons during the 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

"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