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With the increasing adoption of electronic health records, there is anincreasing interest in developing individualized treatment rules, whichrecommend treatments according to patients' characteristics, from largeobservational data. However, there is a lack of valid inference procedures forsuch rules developed from this type of data in the presence of high-dimensionalcovariates. In this work, we develop a penalized doubly robust method toestimate the optimal individualized treatment rule from high-dimensional data.We propose a split-and-pooled de-correlated score to construct hypothesis testsand confidence intervals. Our proposal utilizes the data splitting to conquerthe slow convergence rate of nuisance parameter estimations, such asnon-parametric methods for outcome regression or propensity models. Weestablish the limiting distributions of the split-and-pooled de-correlatedscore test and the corresponding one-step estimator in high-dimensionalsetting. Simulation and real data analysis are conducted to demonstrate thesuperiority of the proposed method.

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score