Multiaccurate Estimators Can Be Simultaneously Robust and Efficient
David Bruns-Smith, Zhongming Xie, Avi Feller
Multiaccuracy, developed in fair machine learning, provides a framework for reducing predictive bias uniformly over subpopulations defined by an auditing class. Recent work shows that multiaccurate estimators trained only on source data can remain low-bias under unknown covariate shifts—a property known as "Universal Adaptability". Building on this, we show that when the auditing class is a Hilbert space, a simple multiaccurate ridge-boosting estimator is numerically equivalent to an ``automatic debiased machine learning'' estimator. As a result, it inherits semiparametric efficiency and optimal asymptotic variance. In this setting, the multiaccurate estimator is not only robust under unknown shifts --- it is also efficient, even without access to data from the target distribution.