Annie Qu
Chancellor’s Professor, Department of Statistics, University of California Irvine
Ph.D., Statistics, the Pennsylvania State University

The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt one of two or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this talk, we propose a novel double-encoder framework to estimate the individualized treatment rule for combination treatments. The proposed method incorporates the interaction effects among different treatments and utilizes correlations among different combinations. In theory, we show that the proposed method achieves a faster convergence rate of the value reduction bound in terms of the number of treatments. Our simulation studies show that the proposed method outperforms the existing ITR estimation in various settings. We also demonstrate the superior performance of the proposed method in a real data application that recommends optimal combination treatments for Type-2 diabetes patients.