Neural State-Space Modeling with Latent Causal-Effect Disentanglement

TL;DR

This paper introduces a neural state-space model that disentangles hidden causal interventions from observed time-series data, enabling the detection of subtle local activities that cause larger global effects, demonstrated on cardiac electrical signals.

Contribution

It presents a novel neural formulation of state-space models that explicitly models and disentangles latent causal effects via neural ODEs, addressing the challenge of uncovering subtle local activities.

Findings

Successfully reconstructed ectopic foci in cardiac signals

Demonstrated causal-effect disentanglement in neural state-space models

Improved detection of local activities with minimal signal strength

Abstract

Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due to their negligible contribution to the optimization loss. Such local activities however can signify important abnormal events in physiological systems, such as an extra foci triggering an abnormal propagation of electrical waves in the heart. We discuss a novel technique for reconstructing such local activity that, while small in signal strength, is the cause of subsequent global activities that have larger signal strength. Our central innovation is to approach this by explicitly modeling and disentangling how the latent state of a system is influenced by potential hidden internal interventions. In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state. Because the intervention can not be directly observed but have to be disentangled from the observed subsequent effect, we integrate knowledge of the native intervention-free dynamics of a system, and infer the hidden intervention by assuming it to be responsible for differences observed between the actual and hypothetical intervention-free dynamics. We demonstrated a proof-of-concept of the presented framework on reconstructing ectopic foci disrupting the course of normal cardiac electrical propagation from remote observations.