Controlling for Omitted Variable Bias in Deep Neural Networks

arXiv:2608.25930 · stat.ME, cs.CV, cs.LG · Submitted 2026-08-26 · Read on arXiv

stat.ME, cs.CV, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/mpff/cocodeel

Terminology

• Regularization — 11× in this paper · explained in 3 episodes
• AUC — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Interpretability — 5× in this paper · explained in 15 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Additivity — 2× in this paper · explained in 1 episode
• Causal Inference — 2× in this paper · explained in 4 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• AI Attribution — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Residualization — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Challenges in interpretability of additive models
• Invariant Risk Minimization
• MONAI: An open-source framework for deep learning in healthcare

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