[Sds-seminars] [Sds-announce] Talk by Daniel Spielman, Sep. 28 at 4pm, on "Balancing covariates in randomized experiments", DL 220

[Dan Spielman]

I will be giving a talk on Wednesday at 4pm in DL 220 on Balancing
covariates in randomized experiments.

This is part of the Institute for Foundations of Data Science (FDS)
Colloquium (also know at the Kline Tower Institute)

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And, here's the abstract for the talk:

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.
--Dan Spielman
Sterling Professor of Computer Science, Statistics & Data Science, and
Mathematics
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