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

Investigating Sindy As a Tool For Causal Discovery In Time Series Signals