Causality in Statistics Education Award
The ASA is proud to call for nominations of the prize, Causality in Statistics Education, aimed at encouraging the teaching of basic causal inference in introductory statistics courses.
The prize carries a monetary award of $10,000. Originally donated by Judea Pearl and now sponsored by Microsoft Research and Google, the prize is motivated by the growing importance of introducing core elements of causal inference into undergraduate and lower-division graduate classes in statistics. For additional information about the award, see the Amstat News articles at magazine.amstat.org/blog/2012/11/01/pearl/ and http://magazine.amstat.org/blog/2013/08/01/causality-in-stat-edu/.
The prize will be given by the ASA to a person or team that does the most to enhance the teaching and learning of causal inference in statistics. Winners will be announced on or about May 1 each year and presented with the prize at the Joint Statistical Meetings.
Winners will be selected by the members of the prize committee, who will administer 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. These include:
- 1a. Ability to correctly classify problems, assumptions, and claims into two distinct categories: causal vs. associational
- 1b. 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
- 1c. 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
- 1d. 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 1a-d) 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.
The deadline for submission has been extended to March 1, 2016. Submissions 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 and questions should be sent to the ASA office at firstname.lastname@example.org.
Maya Petersen (University of California, Berkeley)
Dennis Pearl (Penn State University, CAUSE, co-chair)
Judea Pearl (University of California, Los Angeles, co-chair)
Felix Elwert (University of Wisconsin-Madison)
Daniel Kaplan (Macalester College)
Michael Posner (Villanova University)
Tyler VanderWeele (Harvard School of Public Health)
Larry Wasserman (Carnegie Mellon University)
2015: To Tyler VanderWeele, Harvard School of Public Health, for his book Explanation in Causal Inference: Methods for Mediation and Interaction, which provides a comprehensive and in-depth exposition of fundamental aspects in statistical methodology.
2014: To Maya Petersen and Laura B. Balzer for developing a path-blazing course "Introduction to Causal Inference" at the University of California, Berkeley. With clear lectures, detailed discussion assignments and innovative labs and homework using R, Petersen and Balzer have prepared a new generation of scientists, who have acquired the tools of modern causal analysis and are equipped to tackle each step of the causal roadmap. The course is publicly available online, at www.ucbbiostat.com and thereby provides other institutes an excellent educational resource of this foundational material. Peterson and Balzer's course was chosen primarily on the basis of its "teachability" and its appeal to a broad range of statistics-minded disciplines.
2013: Felix Elwert, University of Wisconsin-Madison, for his innovative two-day course, Causal Inference with Directed Acyclic Graphs.
Past Honorable Mentions
2013: Tyler VanderWeele, Harvard School of Public Health, for his class, Methods for Mediation and Interaction.
2013: Richard Scheines and (the late) Steven Klepper of Carnegie Mellon University for their course, Empirical Research Methods for the Social Sciences.