Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates

Abstract

Controlled experiments are widely applied in many areas such as clinical trials or user behavior studies in IT companies. Recently, it is popular to study experimental design problems to facilitate personalized decision making. In this paper, we investigate the problem of optimal design of multiple treatment allocation for personalized decision making in the presence of observational covariates associated with experimental units (often, patients or users). We assume that the response of a subject assigned to a treatment follows a linear model which includes the interaction between covariates and treatments to facilitate precision decision making. We define the optimal objective as the maximum variance of estimated personalized treatment effects over different treatments and different covariates values. The optimal design is obtained by minimizing this objective. Under a semi-definite program reformulation of the original optimization problem, we use a YALMIP and MOSEK based optimization solver to provide the optimal design. Numerical studies are provided to assess the quality of the optimal design.

Editorial. Design and Analysis of Experiments for Data Science

“…Personalized decision-making in controlled experiments is of growing interest, particularly in clinical trials and user behavior studies. Li et al [5] tackle the challenge of optimizing treatment allocation while considering observational covariates. The proposed method seeks to maximize the variance of personalized treatment effects, enhancing precision in decision-making processes.…”