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2017 Causality in Statistics Award Winner Announced

1 August 2017 185 views No Comment

The American Statistical Association will award the fifth Causality in Statistics Education Prize to Ilya Shpitser, John C. Malone Assistant Professor of Computer Science at The Johns Hopkins University, at the 2017 Joint Statistical Meetings in Baltimore.

Judea Pearl

Ilya Shpitser

2017 Prize Committee Members

Onyebuchi Arah, University of California, Los Angeles
Maya Petersen, University of California, Berkeley
Dennis Pearl (co-chair), Penn State University
Judea Pearl (co-chair), University of California, Los Angeles
Felix Elwert, University of Wisconsin-Madison
Daniel Kaplan, Macalester College
Michael Posner, Villanova University
Arvid Sjölander, Karolinska Institutet
Tyler VanderWeele, Harvard School of Public Health

Established in 2013 to “encourage the teaching of basic causal inference methods in introductory statistics courses” from a donation by Judea Pearl—recipient of the 2012 Turing Award and professor of computer science and statistics at the University of California, Los Angeles—the annual award recognizes the work of an individual or team that enhances the teaching and learning of causal inference in introductory statistics coursework. This year, the $5,000 award is being funded by Microsoft Research and Google.

“While the study and practice of statistics is growing in popularity and demand in both academia and professional occupations, there remains a glaring gap when it comes to causal inference,” said Pearl, who is co-chair of the prize-selection committee. “Even with the recent development of causal inference tools, which are currently sweeping new insights and application areas, most statistics educators and textbooks do not provide any information on these tools,” he continued. “In giving this award, we not only recognize the dynamic efforts of renowned scholars, but also show other researchers and scientists that teaching causal inference can be fun and formative.”

Ilya Shpitser has developed master’s-level graduate course material that takes causal inference from the ivory towers of research to the statistics student with a machine learning and data science background. It combines techniques of graphical and counterfactual models and provides both an accessible coverage of the field and excellent conceptual, computational, and project-oriented exercises for students.

The winning materials of previous Causality in Statistics Education Award winners will be available to download in the early fall. Nominations for the 2018 award are being accepted until February 15, 2018.

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