Pragmatic DML with AI-Learned Representations

arXiv:2610.01935 · econ.EM, stat.ML · Submitted 2026-10-01 · Read on arXiv

econ.EM, stat.ML

Submitted: 2026-10-01

Updated: 2026-10-02

Terminology

• Aggregation — 43× in this paper · explained in 1 episode
• Adaptivity — 20× in this paper · explained in 1 episode
• Consistency — 17× in this paper · explained in 3 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Embeddings — 11× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Causal Inference — 3× in this paper · explained in 4 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Automatic Debiased Machine Learning via Riesz Regression
• Efficient Estimation of Word Representations in Vector Space

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