Deep learning is a powerful set of techniques for detecting complex patterns in data. However, when the causal structure of that process is underspecified, deep learning models can be brittle, lacking robustness to shifts in the distribution of the data-generating process. In this paper, we turn to loop polarity analysis as a tool for specifying the causal structure of a data-generating process, in order to encode a more robust understanding of the relationship between system structure and system behavior within the deep learning pipeline. We use simulated epidemic data based on an SIR model to demonstrate how measuring the polarity of the different feedback loops that compose a system can lead to more robust inferences on the part of neural networks, improving the out-of-distribution performance of a deep learning model and infusing a system-dynamics-inspired approach into the machine learning development pipeline.

翻译：深度学习是一套用于检测数据中复杂模式的强大技术。然而，当数据生成过程的因果结构欠定时，深度学习模型可能变得脆弱，缺乏对数据生成过程分布变化的鲁棒性。本文采用循环极性分析作为工具来明确数据生成过程的因果结构，从而在深度学习流程中编码对系统结构与系统行为之间关系更鲁棒的理解。我们使用基于SIR模型的模拟流行病数据来证明，通过测量构成系统的不同反馈环路的极性，可以使神经网络做出更鲁棒的推断，从而提升深度学习模型的分布外性能，并将受系统动力学启发的思想注入机器学习开发流程。