Synthetic Data Generation for Economists
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Abstract (arXiv)
As more tech companies engage in rigorous economic analyses, we are confronted with a data problem: in-house papers cannot be replicated due to use of sensitive, proprietary, or private data. Readers are left to assume that the obscured true data (e.g., internal Google information) indeed produced the results given, or they must seek out comparable public-facing data (e.g., Google Trends) that yield similar results. One way to ameliorate this reproducibility issue is to have researchers release synthetic datasets based on their true data; this allows external parties to replicate an internal researcher's methodology. In this brief overview, we explore synthetic data generation at a high level for economic analyses.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2020.
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Citing papers per year: 2023: 2; 2024: 1.
3 papers.
- 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 19
- 2023On the causality-preservation capabilities of generative modellingcited by 1
- 2023Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settingscited by 0
- 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics12 Jan 2024
- 2023Evaluating the Utility of GAN Generated Synthetic Tabular Data for Class Balancing and Low Resource Settings24 Jun 2023
- 2023On the causality-preservation capabilities of generative modelling3 Jan 2023