Title: Efficient learning and evaluation of individualized treatment rules under data fusion
Authors: Alex Luedtke - University of Washington (United States) [presenting]
Abstract: The aim is to fuse data from multiple sources together to learn and make inferences about generic smooth summaries of an individualized treatment rule, such as its mean outcome or the proportion of people that it recommends treating. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect, optimal treatment rule, or average reward, with the majority of them merging one historical dataset with covariates, actions, and rewards and one dataset of the same covariates. We consider the general case where multiple datasets align with different parts of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates. We then examine potential gains in efficiency that can arise from fusing these datasets together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. In numerical experiments, we show marked improvements in efficiency from using our proposed estimators compared to their natural alternatives.