GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Studies
Abstract: The Granger framework is useful for discovering causal relations in time-varying signals. Granger causality (GC) tools are mostly developed for densely sampled timeseries data. A substantially different setting, particularly common in population health applications, is the longitudinal study design, where multiple individuals are followed and sparsely observed over time. Longitudinal studies commonly track many variables, which are likely governed by nonlinear dynamics that might have individual-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 model. GLACIAL treats individuals as independent samples and uses the model’s average prediction accuracy on hold-out individuals to probe causal links. Input feature dropout and model interpolation are used to efficiently learn nonlinear dynamic relationships between a large number of variables and to handle missing values respectively. Our experiments on synthetic and real data show GLACIAL outperforming competitive baselines and confirm its utility.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Mingming_Gong1
Submission Number: 828
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