By exploiting the Gram-Schmidt Walk algorithm of Bansal, Dadush, Garg, and Lovett, we can obtain random assignments of low discrepancy. These allow us to obtain more accurate estimates of treatment effects when the information we measure about the subjects is predictive, while also bounding the worst-case behavior when it is not.
In this talk, I will formally explain the problem of estimating treatment effects in randomized controlled trials, the dangers of using fancy inference techniques instead of fancy designs, how we use the Gram-Schmidt Walk algorithm, a tight analysis of this algorithm, and how we use it to obtain confidence intervals. I hope to explain just how much we don't yet know.
This is joint work with Christopher Harshaw, Fredrik Sävje, and Peng Zhang.