Low-Discrepancy, Pseudorandom Experimental Designs
Abstract
We will improve the design of randomized experiments and randomized controlled trials to account for knowledge of subject covariates. The proposed designs will result in more accurate assessment of experimental eects when they are predicted well by the subject covariates, without much loss of accuracy when the eects are unrelated to those covariates.New algorithms will be developed to enable experimenters to sample from thesedesigns, and analyses will be conducted to help experimenters compute the accuracy of the resulting estimators. Our approach to experimental design builds on recent developments in algorithmic discrepancy theory to accommodate moderate and high dimensional covariate information.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 08, 2020
- Source ID
- N000142012335
Entities
People
- Dan Spielman
Organizations
- Office of Naval Research
- United States Navy
- Yale University