Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements. However, most progress has focused on proving identifiability results in different settings, and we are not aware of any successful real-world application. At the same time, the field of dynamical systems benefited from deep learning and scaled to countless applications but does not allow parameter identification. In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. At the same time, we can leverage scalable differentiable solvers developed for differential equations to build models that are both identifiable and practical. Overall, we learn explicitly controllable models that isolate the trajectory-specific parameters for further downstream tasks such as out-of-distribution classification or treatment effect estimation. We experiment with a wind simulator with partially known factors of variation. We also apply the resulting model to real-world climate data and successfully answer downstream causal questions in line with existing literature on climate change.

翻译：因果表征学习有望将因果模型从原始的纠缠测量扩展到隐藏的因果变量。然而，大多数进展集中在证明不同设置下的可识别性结果，我们尚未知晓任何成功的实际应用。与此同时，动力学系统领域受益于深度学习并已扩展至无数应用，但无法实现参数识别。本文明确阐述了两者及其关键假设之间的联系，使我们能够将因果表征学习中开发的可识别方法应用于动力学系统。同时，我们可以利用为微分方程开发的可扩展可微分解算器，构建兼具可识别性与实用性的模型。总体而言，我们学习显式可控的模型，这些模型能够分离轨迹特定参数，以用于下游任务，如分布外分类或处理效应估计。我们通过一个具有部分已知变异因子的风场模拟器进行实验。我们还将所得模型应用于真实世界的气候数据，并成功回答了与现有气候变化文献一致的下游因果问题。