Economists develop theoretical framework for pre-trained embeddings

Researchers have developed a theoretical framework for using pre-trained deep learning embeddings in econometrics, addressing challenges where models are trained on different datasets or tasks. The paper provides conditions for reliable use and analyzes estimation errors, deriving convergence rates for models with pre-trained embeddings. This approach is demonstrated through applications in double machine learning for estimating parameters in partially linear regression, demand estimation using image and text data, missing data imputation, and average treatment effect estimation with unstructured confounders. AI

IMPACT Provides a theoretical foundation for using pre-trained embeddings in economics, potentially enabling more robust analysis of unstructured data.

RANK_REASON The item is an academic paper detailing a new theoretical framework and methodology for applying pre-trained embeddings in econometrics. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• average treatment effect
• deep learning
• Double Machine Learning
• econometrics
• economics
• machine learning
• Partially Linear Regression
• Pre-Trained Embeddings
• unstructured data

• paper
• other

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