mlsanityData to 7 Oct 2026

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

arXiv 2210.07416 ↗First posted 13 Oct 2022cs.LG stat.MEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 79 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

16 of 79 references.

References · 16

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4 more
  1. 2016Entropic Causal Inferencecited by 27
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Causal Transfer in Medical Image Analysiscited by 0