GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies

TL;DR

GLACIAL introduces a novel method combining Granger causality with neural forecasting to analyze causal relationships in sparse, longitudinal medical imaging data, effectively handling missing data and nonlinear dynamics.

Contribution

The paper presents GLACIAL, a new approach that adapts Granger causality for longitudinal studies using neural networks, addressing challenges like sparse sampling and missing data.

Findings

GLACIAL outperforms baseline methods in simulations.

It effectively captures nonlinear causal relationships.

Demonstrates utility on real medical imaging data.

Abstract

The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled timeseries data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over time. Longitudinal studies commonly track several biomarkers, which are likely governed by nonlinear dynamics that might have subject-specific idiosyncrasies and exhibit both direct and indirect causes. Furthermore, real-world longitudinal data often suffer from widespread missingness. GC methods are not well-suited to handle these issues. In this paper, we propose an approach named GLACIAL (Granger and LeArning-based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model. GLACIAL treats subjects as independent samples and uses the model's average prediction accuracy on hold-out subjects to probe causal links. Input dropout and model interpolation are used to efficiently learn nonlinear dynamic relationships between a large number of variables and to handle missing values respectively. Extensive simulations and experiments on a real longitudinal medical imaging dataset show GLACIAL beating competitive baselines and confirm its utility. Our code is available at https://github.com/mnhng/GLACIAL.