论文标题
具有高维协变量的随机对照试验中的基于经验可能性的估计和推断
Empirical Likelihood-Based Estimation and Inference in Randomized Controlled Trials with High-Dimensional Covariates
论文作者
论文摘要
在本文中，我们提出了一种基于数据自适应的经验可能性的方法来进行治疗效果估计和推理，从而克服了传统的基于经验可能性的方法在高维环境中的障碍，通过采用惩罚性回归和机器学习方法来模拟协方差关系。特别是，我们表明我们的程序通过利用数据分解技术成功地恢复了张治疗效应估计量（Zhang，2018）的真实方差。我们提出的估计量在轻度的规律性条件下被证明是渐近正常和半参数有效的。模拟研究表明，我们的估计器比Wager等人提出的估计量更有效。 （2016）当使用随机森林来对协变量结果建模时。此外，当强加了多个机器学习模型时，我们的估计器至少与任何具有单个机器学习模型的常规估计器一样有效。我们使用ACTG175数据和GSE118657数据将我们的方法与现有方法进行比较，并确认我们方法的出色性能。
In this paper, we propose a data-adaptive empirical likelihood-based approach for treatment effect estimation and inference, which overcomes the obstacle of the traditional empirical likelihood-based approaches in the high-dimensional setting by adopting penalized regression and machine learning methods to model the covariate-outcome relationship. In particular, we show that our procedure successfully recovers the true variance of Zhang's treatment effect estimator (Zhang, 2018) by utilizing a data-splitting technique. Our proposed estimator is proved to be asymptotically normal and semiparametric efficient under mild regularity conditions. Simulation studies indicate that our estimator is more efficient than the estimator proposed by Wager et al. (2016) when random forests are employed to model the covariate-outcome relationship. Moreover, when multiple machine learning models are imposed, our estimator is at least as efficient as any regular estimator with a single machine learning model. We compare our method to existing ones using the ACTG175 data and the GSE118657 data, and confirm the outstanding performance of our approach.
