Uncut

Shortcut learning is often handled through fairness or debiasing methods, but this paper connects the issue to the statistical idea of control variables. That…

Controlling for Omitted Variable Bias in Deep Neural Networks

#12 · paper · Manuel Pfeuffer, Roshan Prakash Rane, Kerstin Ritter, Sonja Greven ·

A deep-learning method for controlling known confounders and omitted-variable bias in neural predictions.

Shortcut learning is often handled through fairness or debiasing methods, but this paper connects the issue to the statistical idea of control variables. That framing could give deep-learning practitioners a more explicit way to manage known confounders in image-based prediction.

new arXiv release with linked GitHub repo

uncovered in mainstream sources

Source: https://arxiv.org/abs/2608.25930v1

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