# Deep Learning for Economists

arXiv: https://arxiv.org/abs/2407.15339 · HTML: https://mlsanity.com/paper/2407.15339/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [Cross-Prediction-Powered Inference](https://mlsanity.com/paper/2309.16598/) (2023)
- [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://mlsanity.com/paper/1712.09089/) (2017)
- [(Almost) All of Entity Resolution](https://mlsanity.com/paper/2008.04443/) (2020)
- [Noise-Robust De-Duplication at Scale](https://mlsanity.com/paper/2210.04261/) (2022)
- [Neural OCR Post-Hoc Correction of Historical Corpora](https://mlsanity.com/paper/2102.00583/) (2021)
- [Global Entity Disambiguation with BERT](https://mlsanity.com/paper/1909.00426/) (2019)
- [WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets](https://mlsanity.com/paper/2010.08232/) (2020)
- [Efficient OCR for Building a Diverse Digital History](https://mlsanity.com/paper/2304.02737/) (2023)
- [EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge](https://mlsanity.com/paper/2310.10050/) (2023)
- [Quantifying Character Similarity with Vision Transformers](https://mlsanity.com/paper/2305.14672/) (2023)

## Built on by (most cited first, 4 of 4)

- [Mining Causality: AI-Assisted Search for Instrumental Variables](https://mlsanity.com/paper/2409.14202/) (2024)
- [The Measurement Revolution? Credible Measurement and Inference in the Age of AI](https://mlsanity.com/paper/2608.23524/) (2026)
- [Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction](https://mlsanity.com/paper/2509.10802/) (2025)
- [Chronos: The AI Co-Historian](https://mlsanity.com/paper/2604.03553/) (2026)

## Abstract (arXiv)

Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.