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.

我们将因果表示学习与动力系统以及其假设联系起来，应用在可辨识的方法上，建立可辨识且实用的模型，从而解决下游任务，如分布外分类和治疗效果估计，并在真实气候数据中成功回答与现有气候变化文献相符的下游因果问题。