A single gradient-regularized neural forecaster, combined with a phase-randomized surrogate significance test, recovers Granger causal structure from multivariate time series better than several baselines in the reported benchmarks.
Causal-trivial attention graph neural network for fault diagnosis of complex industrial processes,
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A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes
A single gradient-regularized neural forecaster, combined with a phase-randomized surrogate significance test, recovers Granger causal structure from multivariate time series better than several baselines in the reported benchmarks.