mlsanityData to 7 Oct 2026

Data Augmentation via Causal-Residual Bootstrapping

arXiv 2603.15335 ↗First posted 16 Mar 2026cs.LGcited by 0

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Abstract (arXiv)

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. We suggest a related approach for settings with additive noise that can incorporate information beyond a Markov equivalence class. The approach, built on the principle of independent mechanisms, permutes the residuals of models built on marginal probability distributions. Predictive models built on our augmented data demonstrate improved accuracy, for which we provide theoretical backing in linear Gaussian settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 16 Mar 2026.

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References · 27

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15 more
  1. 2017Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributionscited by 125
  2. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
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  8. 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcited by 10
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  10. 2020Robust Correction of Sampling Bias Using Cumulative Distribution Functionscited by 6
  11. 2024Local Causal Discovery for Structural Evidence of Direct Discriminationcited by 6
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  13. 2023A Guide for Practical Use of ADMG Causal Data Augmentationcited by 2
  14. 2023Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shiftscited by 2
  15. 2025Meta-Dependence in Conditional Independence Testingcited by 1

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