This article introduces a new framework for estimating average treatment
effects under unobserved confounding in modern data-rich environments featuring
large numbers of units and outcomes. The proposed estimator is doubly robust,
combining outcome imputation, inverse probability weighting, and a novel
cross-fitting procedure for matrix completion. We derive finite-sample and
asymptotic guarantees, and show that the error of the new estimator converges
to a mean-zero Gaussian distribution at a parametric rate. Simulation results
demonstrate the practical relevance of the formal properties of the estimators
analyzed in this article.

提出了一种在现代数据丰富的环境中估计未观察到的混杂影响下的平均处理效应的新框架，该框架具有大量的单位和结果。该提出的估计器具有双重鲁棒性，结合了结果填充、逆概率加权和用于矩阵补全的新型交叉拟合程序。我们推导出有限样本和渐近保证，并且证明新估计器的误差以参数速率收敛到均值为零的高斯分布。模拟结果显示本文分析的估计器的形式性质的实际相关性。