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（PCL）是一种在存在未观测混杂因素时，利用代理变量（结构化辅助信息）估计处理对结果因果效应的方法。该方法通过两阶段回归实现：第一阶段建模处理变量与代理变量之间的关系；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影响。PCL在满足可辨识条件时，可保证恢复真实因果效应。我们提出了一种新的PCL方法——深度特征代理变量法（DFPV），用于处理代理变量、处理变量和结果变量均为高维且存在深度神经网络特征表征的非线性复杂关系的情形。实验表明，在包括高维图像数据场景在内的挑战性合成基准测试中，DFPV优于现有最优PCL方法。此外，我们证明了PCL可应用于混杂赌博机问题的离线策略评估，其中DFPV同样展现出竞争性性能。