[Statlist] Reminder: Stiefel Lecture 2020 (postponed), October 7, 2022, ETH Zurich, HG F 30, 17:15

Maurer Letizia |et|z|@m@urer @end|ng |rom ethz@ch
Mon Oct 3 11:06:15 CEST 2022


ETH Foundations of Data Science (ETH-FDS) announces the Stiefel Lecture 2020 (postponed) 

delivered by Dan Spielman, Yale University, on 

October 7, 2022, 
ETH Zurich, 
HG F 30 at 17:15.

Titel: Balancing covariates in randomized experiments

Abstract: In randomized experiments, we randomly assign the treatment that each experimental subject receives. Randomization can help us accurately estimate the difference in treatment effects with high probability. It also helps ensure that the groups of subjects receiving each treatment are similar. If we have already measured characteristics of our subjects that we think could influence their response to treatment, then we can increase the precision of our estimates of treatment effects by balancing those characteristics between the groups. We show how to use the recently developed Gram-​Schmidt Walk algorithm of Bansal, Dadush, Garg, and Lovett to efficiently assign treatments to subjects in a way that balances known characteristics without sacrificing the benefits of randomization. These allow us to obtain more accurate estimates of treatment effects to the extent that the measured characteristics are predictive of treatment effects, while also bounding the worst-​case behavior when they are not. This is joint work with Chris Harshaw, Fredrik Sävje, and Peng Zhang.

The Stiefel Lecture has been created in honor of Eduard Stiefel (1909-1978) who was professor of mathematics at ETH Zürich. Stiefel has been the driving force for establishing "electronic scientific computing" with ERMETH (Elektronische Rechenmaschine der ETH). This became a landmark in computational and mathematical sciences with a huge impact to a broad range of applications in engineering and natural science. Stiefel has made fundamental and lasting contributions in mathematics, including the introduction of the Stiefel-Whitney classes, the Stiefel manifold and the conjugate gradient method. Stiefel advised 63 PhD students, many of whom became leaders in their field. 

We cordially invite you to attend this Stiefel Lecture. There’s no registration required.

The talk will be followed by an apero. 

Looking forward to welcoming you at this special event.

With kind regards,

Peter Bühlmann
Director
ETH Foundations of Data Science


Further information and details can be found at:
https://math.ethz.ch/sfs/eth-foundations-of-data-science.html
https://math.ethz.ch/sfs/news-and-events/stiefel-lectures.html
https://library.ethz.ch/standorte-und-medien/plattformen/kurzportraets/eduard-stiefel-1909-1978.html
http://www.ethistory.ethz.ch/rueckblicke/departemente/dinfk/forschung/




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