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

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2022-01-01
Muxuan Liang, Young-Geun Choi, Yang Ning, Maureen A Smith, Ying-Qi Zhao
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引用次数: 0

Abstract

With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.

使用分割和池化去相关分数对观察数据中的高维个体化治疗规则进行估计和推断。
随着电子健康记录的应用日益广泛,人们越来越关注从大型观察数据中开发个性化治疗规则,根据患者的特征推荐治疗方法。然而,在存在高维协变量的情况下,从这类数据中制定的规则缺乏有效的推断程序。在这项工作中,我们开发了一种惩罚性双重稳健方法,用于从高维数据中估计最优个体化治疗规则。我们提出了一种拆分和池化去相关分数来构建假设检验和置信区间。我们的建议采用数据拆分来克服骚扰参数估计收敛速度慢的问题,如结果回归或倾向模型的非参数方法。我们在高维环境中建立了拆分和池化去相关分数检验的极限分布和相应的一步估计器。通过模拟和实际数据分析,证明了所提方法的优越性。
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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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