AI RESEARCH
Data Augmentation via Causal-Residual Bootstrapping
arXiv CS.LG
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ArXi:2603.15335v1 Announce Type: new Data augmentation integrates domain knowledge into a dataset by making domain-informed modifications to existing data points. For example, image data can be augmented by duplicating images in different tints or orientations, thereby incorporating the knowledge that images may vary in these dimensions. Recent work by Teshima and Sugiyama has explored the integration of causal knowledge (e.g, A causes B causes C) up to conditional independence equivalence.