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

TL;DR

This paper explores enhancing the SINDy algorithm with causal discovery tools to improve the robustness and accuracy of identifying governing equations in time series data.

Contribution

It introduces a novel approach combining SINDy with causal discovery methods to improve causal inference in dynamical systems.

Findings

Augmenting SINDy with causal discovery improves system identification.

The combined method yields more causally robust models.

Empirical results demonstrate enhanced performance in time series analysis.

Abstract

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.