i6092467/GVAR
An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.
This framework helps researchers and data scientists analyze complex time series data to understand how different variables influence each other over time. It takes in multivariate time series data and outputs not just which variables have a causal effect, but also the direction (positive or negative) of that influence and how it changes. This tool is designed for anyone needing to interpret causal relationships in dynamic systems, like neuroscientists studying fMRI data or ecologists modeling population dynamics.
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Use this if you need to determine causal relationships in your time series data and want to understand the direction and variability of those effects, rather than just knowing if a connection exists.
Not ideal if you do not have access to a CUDA-enabled GPU or if you primarily need a simple, non-interpretable method for causality detection.
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Python
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Last pushed
Apr 05, 2023
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