Statistical prediction models are often trained on data 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 answer counterfactual questions that extract stability-enhancing information from proxies. We demonstrate the effectiveness of our techniques on synthetic and real data.

翻译：统计预测模型通常基于与最终应用场景概率分布不同的数据进行训练。一种主动应对这些偏移的方法利用因果机制在环境间保持不变的直觉。本文聚焦于目标变量的因果与反因果变量均不可观测的挑战性设定。基于信息论，我们为可观测的下游变量开发了特征选择与工程技术，使其充当代理变量。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回答反事实问题，从而从代理中提取增强稳定性的信息。我们在合成数据与真实数据上验证了所提技术的有效性。