  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.
