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淆变量的情况下，利用混淆变量的代理（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：在第一阶段，我们对处理与代理之间的关系进行建模；在第二阶段，我们利用该模型，在代理提供的上下文条件下，学习处理对结果的影响。在可识别性条件满足的前提下，PCL能够保证真实因果效应的恢复。针对代理、处理与结果均为高维且存在非线性复杂关系（以深度神经网络特征表示）的情况，我们提出了一种新颖的PCL方法——深度特征代理变量法（DFPV）。实验表明，在包括涉及高维图像数据场景在内的挑战性合成基准测试中，DFPV优于近期最先进的PCL方法。此外，我们证明了PCL可应用于混淆多臂老虎机问题的离线策略评估，在该场景中DFPV同样展现出具有竞争力的性能。