I recently shared an underappreciated use case for generative models in data science: creating high-fidelity tabular datasets (OTA data for regression discontinuity).<\/p>\n
The model\u2019s success in data synthesis motivated a question: what are some high-value use cases for data science teams when using generative models to create datasets? This, in turn, led to our latest Data Duets post: \u201cUsing generative models, well, to generate data\u201d<\/p>\n
I walk through using the Synthetic Data Vault to scale a small OTA sample while preserving its statistical properties and the causal discontinuity. Duygu Dagli<\/a> then weighs in on business implications: creating statistical twins to share data with vendors for solution optimization and benchmarking, simulating product recall data, and solving cold start problems in retail.<\/p>\n Ultimately the approach here represents a step toward data centricity: using high-fidelity simulations to dissect and validate the assumptions that drive our models.<\/p>\n