  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.
