The technique of data augmentation (DA) is often used in machine learning for regularization purposes to better generalize under i.i.d. settings. In this work, we present a unifying framework with topics in causal inference to make a case for the use of DA beyond just the i.i.d. setting, but for generalization across interventions as well. Specifically, we argue that when the outcome generating mechanism is invariant to our choice of DA, then such augmentations can effectively be thought of as interventions on the treatment generating mechanism itself. This can potentially help to reduce bias in causal effect estimation arising from hidden confounders. In the presence of such unobserved confounding we typically make use of instrumental variables (IVs) -- sources of treatment randomization that are conditionally independent of the outcome. However, IVs may not be as readily available as DA for many applications, which is the main motivation behind this work. By appropriately regularizing IV based estimators, we introduce the concept of IV-like (IVL) regression for mitigating confounding bias and improving predictive performance across interventions even when certain IV properties are relaxed. Finally, we cast parameterized DA as an IVL regression problem and show that when used in composition can simulate a worst-case application of such DA, further improving performance on causal estimation and generalization tasks beyond what simple DA may offer. This is shown both theoretically for the population case and via simulation experiments for the finite sample case using a simple linear example. We also present real data experiments to support our case.

翻译：数据增强（DA）技术常被用于机器学习中的正则化目的，以在独立同分布（i.i.d.）设定下实现更好的泛化。本文提出一个融合因果推断主题的统一框架，论证了DA不仅适用于i.i.d.设定，还可用于跨干预的泛化。具体而言，我们证明当结果生成机制对DA的选择具有不变性时，此类增强可被视作对治疗生成机制本身的干预。这有助于减少由隐藏混杂因子引起的因果效应估计偏差。在存在此类未观测混杂的情况下，我们通常使用工具变量（IVs）——即那些在给定条件下与结果独立的治疗随机化来源。然而，在许多应用中IVs可能不如DA容易获得，这正是本研究的主要动机。通过对基于IV的估计量进行适当正则化，我们引入了类工具变量（IVL）回归的概念，用于在放宽某些IV属性的情况下缓解混杂偏差并提升跨干预的预测性能。最后，我们将参数化DA构建为IVL回归问题，并证明当组合使用时，可以模拟此类DA的最坏情况应用，从而在因果估计和泛化任务上取得比简单DA更优的性能。这一结论通过总体情况的理论分析以及使用简单线性示例的有限样本模拟实验得到验证。我们还提供了真实数据实验以支持本研究的论点。