发表机构
Vanderbilt University; Vanderbilt University Medical Center（范德堡大学; 范德堡大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于神经网络和Neyman-正交得分的去偏机器学习估计器，用于部分线性泊松模型中的计数数据处理，以减少偏差并实现有效推断。
AI 中文摘要
我们开发了一种新颖的去偏机器学习（DML）估计器来分析计数数据，这类数据在临床或生物医学研究中很常见。具体而言，我们使用部分线性泊松模型来估计二元处理对计数结果的影响。该模型的一个关键优势是，它通过一个干扰函数来表示复杂的协变量结构，该函数使用神经网络进行灵活估计。我们使用Neyman-正交得分函数来构造估计器，以降低其对干扰函数估计误差的敏感性。采用交叉拟合来减轻神经网络中的过拟合偏差。在温和的正则条件下，该DML估计器具有渐近正态性，收敛速度为根号n。我们推导出闭式方差估计器，并为处理效应构建了Wald置信区间。大量模拟表明，与广义线性模型和增强逆概率加权（AIPW）估计器相比，在大多数设置下，所提出的估计器减少了偏差和均方根误差，同时实现了接近名义水平的覆盖率。所提出的程序已在R包PoissonDML中实现。我们将该方法应用于一个合成的HIV队列数据，以研究处理对7141名分析患者的艾滋病定义事件计数的影响。我们的方法在部分线性泊松模型下为处理效应提供了实用且有效的估计和推断，同时结合现代机器学习方法来估计复杂的协变量结构。
英文摘要
We develop a novel debiased machine learning (DML) estimator to analyze count data, which are common in clinical or biomedical studies. Specifically, we estimate the effect of a binary treatment on count outcomes using a partially linear Poisson model. A key advantage of this model is that it represents the complex covariate structure through a nuisance function, which is estimated flexibly using neural networks. We use a Neyman-orthogonal score function to construct the estimator, reducing its sensitivity to errors in estimating the nuisance function. Cross-fitting is used to mitigate overfitting bias in neural networks. Under mild regularity conditions, this DML estimator is asymptotically normal with root-$n$ convergence. We derive a closed-form variance estimator and construct a Wald confidence interval for the treatment effect. Extensive simulations demonstrate that the proposed estimator reduces bias and root mean square error compared with the generalized linear model and the augmented inverse probability weighting (AIPW) estimator in most settings, while achieving a coverage probability near the nominal level. The proposed procedure is implemented in the R package $\texttt{PoissonDML}$. We apply our approach to a synthetic HIV cohort data to investigate the effect of treatment on AIDS-defining event counts of 7,141 analyzed patients. Our approach provides practical and valid estimation and inference for treatment effects under the partially linear Poisson model while combining modern machine learning methods to estimate complex covariate structures.
