CaDrift: A Time-dependent Causal Generator of Drifting Data Streams

Ali Nemati5 days ago24 sec read29 views

Researchers introduced CaDrift, a synthetic data generation framework that creates evolving data streams using Structural Causal Models to simulate distributional and covariate shifts. This tool is crucial for evaluating machine learning models' performance under changing conditions, offering content creators and researchers a means to test and improve their algorithms in dynamic environments.

Read the full article at arXiv cs.LG (ML)

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