Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder. This is achieved via two-stage regression: in the first stage, we model relations among the treatment and proxies; in the second stage, we use this model to learn the effect of treatment on the outcome, given the context provided by the proxies. PCL guarantees recovery of the true causal effect, subject to identifiability conditions. We propose a novel method for PCL, the deep feature proxy variable method (DFPV), to address the case where the proxies, treatments, and outcomes are high-dimensional and have nonlinear complex relationships, as represented by deep neural network features. We show that DFPV outperforms recent state-of-the-art PCL methods on challenging synthetic benchmarks, including settings involving high dimensional image data. Furthermore, we show that PCL can be applied to off-policy evaluation for the confounded bandit problem, in which DFPV also exhibits competitive performance.

翻译：代理因果学习是一种在存在未观测混杂因素时估计处理对结果因果效应的方法，该方法利用代理变量（结构化辅助信息）替代混杂因子。这通过两阶段回归实现：第一阶段建立处理与代理变量之间的关系模型；第二阶段利用该模型，在代理变量提供的上下文条件下学习处理对结果的效应。在满足可识别性条件的前提下，代理因果学习能够保证恢复真实因果效应。我们提出一种新型的代理因果学习方法——深度特征代理变量方法，以应对代理变量、处理变量和结果变量均为高维且存在非线性复杂关系的情况（通常表现为深度神经网络的高层特征）。实验表明，在包含高维图像数据等具有挑战性的合成基准测试中，深度特征代理变量方法优于当前最先进的代理因果学习方法。此外，我们证明了代理因果学习可应用于混杂强盗问题的离线策略评估，深度特征代理变量方法在此场景下同样展现出具有竞争力的性能。