Statistical prediction models are often trained on data that is drawn from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focus on a challenging setting in which the causal and anticausal variables of the target are unobserved. Leaning on information theory, we develop feature selection and engineering techniques for the observed downstream variables that act as proxies. We identify proxies that help to build stable models and moreover utilize auxiliary training tasks to extract stability-enhancing information from proxies. We demonstrate the effectiveness of our techniques on synthetic and real data.

翻译：统计预测模型通常在与其最终应用场景不同的概率分布数据上进行训练。一种主动应对这些偏移的方法利用了因果机制应在环境间保持不变的直觉。本文聚焦于一个具有挑战性的场景，其中目标的因果变量与反因果变量均未被观测。基于信息论，我们为充当代理的观测下游变量开发了特征选择与工程技术。我们识别出有助于构建稳定模型的代理，并进一步利用辅助训练任务从代理中提取增强稳定性的信息。我们在合成数据和真实数据上验证了所提技术的有效性。