Optimal Design of Controlled Experiments for Personalized Decision Making in the Presence of Observational Covariates
Volume 1, Issue 3 (2023), pp. 386–393
Pub. online: 26 January 2023
Type: Methodology Article
Open Access
Open Access
Area: Statistical Methodology
Accepted
12 January 2023
12 January 2023
Published
26 January 2023
26 January 2023
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.
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