Active Statistical Inference
This addresses the challenge of efficient data collection for statistical inference in fields like public opinion research, census analysis, and proteomics, offering a novel approach to reduce sample sizes while maintaining validity.
The paper tackles the problem of statistical inference with limited labeling budgets by proposing active inference, a method that uses machine learning to prioritize uncertain data points for labeling, achieving the same accuracy with far fewer samples than non-adaptive baselines.
Inspired by the concept of active learning, we propose active inference$\unicode{x2013}$a methodology for statistical inference with machine-learning-assisted data collection. Assuming a budget on the number of labels that can be collected, the methodology uses a machine learning model to identify which data points would be most beneficial to label, thus effectively utilizing the budget. It operates on a simple yet powerful intuition: prioritize the collection of labels for data points where the model exhibits uncertainty, and rely on the model's predictions where it is confident. Active inference constructs provably valid confidence intervals and hypothesis tests while leveraging any black-box machine learning model and handling any data distribution. The key point is that it achieves the same level of accuracy with far fewer samples than existing baselines relying on non-adaptively-collected data. This means that for the same number of collected samples, active inference enables smaller confidence intervals and more powerful p-values. We evaluate active inference on datasets from public opinion research, census analysis, and proteomics.