Econometrics with Pre-Trained Embeddings for Unstructured Data
econ.EM, math.ST, stat.ML, stat.TH
Submitted: 2026-07-19
Updated: 2026-09-25
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Adventures in Demand Analysis Using AI
- Inference for Regression with Variables Generated by AI or Machine Learning
- Interpretable Discovery from Unstructured Data: A High-Dimensional Approach
- A Unifying Framework for Robust and Efficient Inference with Unstructured Data
- From Unstructured Data to Demand Counterfactuals: Theory and Practice
- Demand Estimation with Text and Image Data
- Automatic Locally Robust GMM with Machine-Learning-Generated Regressors
- Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators
- Copyright and Competition: Estimating Supply and Demand with Unstructured Data
- DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
- The Fragility of Sparsity
- Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
- Causal Inference on Outcomes Learned from Text
- Program Evaluation with Remotely Sensed Outcomes
- Adjustment for Confounding using Pre-Trained Representations
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