Identification and Inference for Algorithmic Frontiers with Selective Labels
The paper characterizes a fairness-accuracy (FA) frontier using statistical tools when outcomes are only observed for selected individuals. It provides point identification methods, such as debiased machine learning estimators based on inverse probability weighting, to estimate this frontier and build confidence sets. This allows researchers to test hypotheses about the frontier's shape and compare it against other theoretical boundaries.