2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp - Department of Statistics - Purdue University Skip to main content

2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp


On July 13 - 15, 2021, the Department of Statistics and the Krannert School of Management co-hosted the inaugural Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp. This event was co-organized by Dr. Mohammad Rahman, Dr. Vinayak Rao, and Dr. Arman Sabbaghi, with 30 in-person attendees and over 300 online attendees registered. Professor Dennis Lin, Head of the Department of Statistics and Distinguished Professor of Statistics, gave a welcome message at the start of the reception. On the first day of the boot camp Dr. Sabbaghi introduced fundamental concepts in causal inference and machine learning approaches for causal inference, and Dr. Stefan Wager from Stanford University gave a presentation on heterogeneous treatment effects. In the morning of the second day Dr. Rahman introduced an Airbnb dataset that was the focus of the interactive afternoon session involving hands-on use of R libraries related to machine learning and causal inference, which was led by Dr. Sabbaghi. Professor Jasjeet Sekhon, the Eugene Meyer Professor of Political Science and of Statistics and Data Science in Yale University, gave the keynote presentation on the second day of the boot camp. Finally, Dr. Richard Hahn from Arizona State University introduced the fundamentals of Bayesian causal inference and related machine learning approaches in the first part of the third day, and Dr. Rao completed the boot camp by leading the hands-on use of Bayesian machine learning aspects of causal inference. 

sabbaghirahmanvarao Pictured left to right: Dr. Arman Sabbaghi, Dr. Mohammad Rahman, Dr. Vinayak Rao

The boot camp brought together Purdue graduate students and faculty from different departments, introducing them to state-of-the-art machine learning algorithms for performing causal inferences on Big Observational Data. The boot camp set the stage for future cross-disciplinary collaborations through the presentations of the external speakers, who are recognized for advancing the frontiers of causal machine learning, and through hands-on sessions involving reproducible codes and analyses on complex data from the online service industry. The co-organizers will continue to organize sessions on causal inference in future academic semesters involving thought leaders in causal inference and faculty members from the Department of Statistics and the Krannert School of Management. One important series of events to be aware of for the Fall 2021 semester is the Distinguished Theme Seminar Series on causal inference. Additional information about this seminar series can be found at the following link: https://www.stat.purdue.edu/theme_seminar_2021/index.html.

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