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Found 2 papers, 2 papers with code
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.
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