REDCLIFF-S models time-varying causal graphs as history-weighted combinations of nonlinear static graphs and reports large F1 gains over baselines in synthetic and brain-data experiments.
Neural additive vector autoregression models for causal discovery in time series
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Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
REDCLIFF-S models time-varying causal graphs as history-weighted combinations of nonlinear static graphs and reports large F1 gains over baselines in synthetic and brain-data experiments.