algorithmic-ml

This paper investigates conditions for algorithmic machine learning (e.g., kNN, random forest) to achieve design-unbiased prediction and classification for finite populations, using probability sampling designs rather than assumed data models. It extends design-based inference from survey sampling to ML algorithms.