Research

We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments. At a high level the problem is to find a clustering of the population such that each part is at least a given size and has at least a given number of elements from each treatment class (so that the experiments are statistically significant), and that all elements within a block are as similar as possible (to improve the accuracy of the analysis). (Read more)