Causality in Statistics Education Award
University of Maryland
Peter Steiner and Julian Schüessler are jointly awarded the 2019 Causality in Statistics Education Award for their respective courses, “Design and Analysis of Quasi-Experiments for Causal Inference” and “Causal Graphs.” Both well-designed courses introduce a range of causal inference concepts accessibly and rigorously to students working in a range of applied sciences, and each makes an independent and complimentary contribution to statistical education in causality.
About the Award
The Causality in Statistics Education award encourages the teaching of basic causal inference in introductory statistics courses and is motivated by the growing importance of introducing core elements of causal inference into undergraduate and lower-division graduate statistics classes. The award is given by the ASA to a person or team that does the most to enhance the teaching and learning of causal inference in statistics.
Established in 2013 by a donation from Judea Pearl and continued by support from Microsoft Research and Google, the award provides for a $5,000 cash prize each year.
Winners are selected by members of the Causality in Statistics Education Award Committee, who consider submissions and judge their merit according to the following criteria:
- The extent to which the material submitted equips students with skills needed for effective causal reasoning, including the following:
- Ability to correctly classify problems, assumptions, and claims into two distinct categories: causal vs. associational
- Ability to take a given causal problem and articulate in some mathematical language (e.g., counterfactuals, equations, or graphs) both the target quantity to be estimated and the assumptions one is prepared to make (and defend) to facilitate a solution
- Ability to determine, in simple cases, whether control for covariates is needed for estimating the target quantity, what covariates need be controlled, what the resulting estimand is, and how it can be estimated using the observed data
- Ability to take a simple scenario (or model), determine whether it has statistically testable implications, and apply data to test the assumed scenario
- The extent to which the submitted material assists statistics instructors in gaining an understanding of the basics of causal inference, as outlined in a–d above, and prepares them to teach these basics in undergraduate and lower-division graduate classes in statistics
Nominated material can be in a variety of forms, including exemplary content such as class notes, books, or chapters with associated lesson plans; excellent resources for teachers such as annotated instruction manuals; or innovative student activities with pedagogical and content notes, especially those using broadly accessible technology.
Award Recipient Responsibilities
The award recipient is responsible for providing a current photograph and general personal information the year the award is presented. The American Statistical Association uses this information to publicize the award and prepare the check and certificate.
Nominations are due by March 1 each year and should include a cover letter that provides information about the nominee, type of material suggested as an important contribution, the intended audience, and an abstract of why the material is nominated, along with the nominated work. Submissions should be sent to the ASA office at firstname.lastname@example.org.
The award is presented at the Joint Statistical Meetings in the same year.
Please contact the ASA office at email@example.com.