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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