GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
This addresses a methodological gap for researchers in medical imaging and longitudinal studies, though it is incremental as it adapts existing frameworks to a specific domain.
The paper tackles the problem of discovering causal relations in sparsely sampled longitudinal medical imaging data, which existing Granger causality methods are ill-suited for, by proposing GLACIAL, a method that combines Granger causality with a multi-task neural forecasting model, and shows it outperforms baselines in simulations and real-world experiments.
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.