The No Unmeasured Confounding Assumption is widely used to identify causal effects in observational studies. Recent work on proximal inference has provided alternative identification results that succeed even in the presence of unobserved confounders, provided that one has measured a sufficiently rich set of proxy variables, satisfying specific structural conditions. However, proximal inference requires solving an ill-posed integral equation. Previous approaches have used a variety of machine learning techniques to estimate a solution to this integral equation, commonly referred to as the bridge function. However, prior work has often been limited by relying on pre-specified kernel functions, which are not data adaptive and struggle to scale to large datasets. In this work, we introduce a flexible and scalable method based on a deep neural network to estimate causal effects in the presence of unmeasured confounding using proximal inference. Our method achieves state of the art performance on two well-established proximal inference benchmarks. Finally, we provide theoretical consistency guarantees for our method.

翻译：在观察研究中,人们广泛使用 " 不测的混杂假设 " 来查明因果关系。最近关于预测推论的工作提供了替代的识别结果,即使在没有观测到的混淆者在场的情况下,也取得了成功。只要一个人测量了足够丰富的代用变量,满足了具体的结构条件。然而,大概的推论需要解决一个不测的整体方程。以前的方法曾使用各种机器学习技术来估计这一整体方程的解决方案,通常称为桥梁功能。然而,以前的工作往往受到以下因素的限制:依赖预先指定的内核功能,这些功能不是数据适应性的,而是在大规模数据集上挣扎。在这项工作中,我们采用了一种基于深层神经网络的灵活和可扩缩的方法,以估计在使用准误判法进行非计量的情况下产生的因果效应。我们的方法在两种既定的准误判基准上达到了艺术性能的状态。最后,我们从理论上保证了我们的方法的一致性。