2024/06/05 by Josuan Calderon, Gordon J. Berman, Calderon, Josuan +2 · 1 voice · 2 citations
Computer Science · Physics and Astronomy · #Model Reduction and Neural Networks #Neural Networks and Applications #Time Series Analysis and Forecasting #cs.LG #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2406.03212
openalex publication_date 2024/06/05 · openalex created_date 2024/06/08 · openalex updated_date 2026/07/28
Most approaches for assessing causality in complex dynamical systems fail when the interactions between variables are inherently non-linear and non-stationary. Here we introduce Temporal Autoencoders for Causal Inference (TACI), a methodology that combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture to identify and measure the direction and strength of time-varying causal interactions. Through tests on both synthetic and real-world datasets, we demonstrate TACI's ability to accurately quantify dynamic causal interactions across a variety of systems. Our findings display the method's effectiveness compared to existing approaches and also highlight our approach's potential to build a deeper understanding of the mechanisms that underlie time-varying interactions in physical and biological systems.