The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.

翻译：辛迪算法已被成功用于从时间序列数据中确定动态系统的治理方程式。 在本文中,我们争辩说,这使得辛迪成为了可能有用的因果发现工具,而现有的因果发现工具可以被用来大大改善辛迪作为强力稀疏模型和系统识别工具的性能。 然后,我们从经验上证明,利用因果发现工具增强辛迪算法可以为工程师提供一种学习因果稳健的治理方程式的工具。