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

• URL: http://arxiv.org/abs/2210.07416v1
• Date: Thu, 13 Oct 2022 23:42:13 GMT
• Title: GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Studies
• Authors: Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu
• Abstract summary: We propose GLACIAL, which stands for "Granger and LeArning-based CausalIty Analysis for Longitudinal studies" GLACIAL treats individuals as independent samples and uses average prediction accuracy on hold-out individuals to test for effects of causal relationships. Extensive experiments on synthetic and real data demonstrate the utility of GLACIAL.
• Score: 19.312260690210458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Granger framework is widely used for discovering causal relationships based on time-varying signals. Implementations of Granger causality (GC) 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 for a limited number of times. 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 intend to fill this methodological gap. We propose to marry the GC framework with a machine learning based prediction model. We call our approach GLACIAL, which stands for "Granger and LeArning-based CausalIty Analysis for Longitudinal studies." GLACIAL treats individuals as independent samples and uses average prediction accuracy on hold-out individuals to test for effects of causal relationships. GLACIAL employs a multi-task neural network trained with input feature dropout to efficiently learn nonlinear dynamic relationships between a large number of variables, handle missing values, and probe causal links. Extensive experiments on synthetic and real data demonstrate the utility of GLACIAL and how it can outperform competitive baselines.

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