Regression-Robust Designs of Controlled Experiments

• Activity Number: 238
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Nathan Kallus*+
• Companies: MIT
• Keywords: Optimal experimental design ; controlled experiments ; nonparametric modelling ; covariate balance

Abstract

Experimental designs that balance pre-treatment measurements (baseline covariates) are in pervasive use throughout the practice of controlled experimentation, including randomized block designs, pairwise-matched designs, and re-randomization. We argue that no balance better than complete randomization can be achieved without partial structural knowledge about the treatment effects and therefore such knowledge must be present in these experiments. Therefore, that balancing designs are in popular use and are advocated means that this knowledge is in fact available to the researcher, just as one would expect from a researcher conducting experiments in her own domain of expertise. We propose a novel framework for formulating such knowledge that recovers these designs as optimal under certain modeling choices and suggests new optimal designs that are based on nonparametric modeling and offer significant gains in precision and power. We characterize the unbiasedness, variance, and consistency of resulting estimators; solve the design problem; and develop appropriate inferential algorithms. We make connections to Bayesian experimental design and extensions to dealing with non-compliance.