Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing

This paper introduces a rigorous statistical framework for discovering human-interpretable insights from unstructured data, such as text, audio, and video. By repurposing AI interpretability tools like sparse autoencoders, the method maps complex data into a high-dimensional space of thousands of distinct concepts. The author utilizes advanced multiple hypothesis testing to ensure these discoveries remain statistically valid while avoiding the pitfalls of data snooping or researcher bias. To ensure the results are understandable, the system employs Large Language Models to generate and evaluate natural language descriptions of the identified patterns. Applications to empirical economics demonstrate that this approach can automatically recover nuanced findings that previously required intensive manual labor or separate experiments. Overall, the framework provides a principled, inexpensive, and replicable way to uncover "unknown unknowns" within large, unstructured datasets.