Multiply Robust Estimation for Local Distribution Shifts with Multiple
  Domains
Steven Wilkins-Reeves Xu Chen Qi Ma Christine Agarwal Aude Hofleitner
Distribution shifts are ubiquitous in real-world machine learning
applications, posing a challenge to the generalization of models trained on one
data distribution to another. We focus on scenarios where data distributions
vary across multiple segments of the entire population and only make local
assumptions about the differences between training and test (deployment)
distributions within each segment. We propose a two-stage multiply robust
estimation method to improve model performance on each individual segment for
tabular data analysis. The method involves fitting a linear combination of the
based models, learned using clusters of training data from multiple segments,
followed by a refinement step for each segment. Our method is designed to be
implemented with commonly used off-the-shelf machine learning models. We
establish theoretical guarantees on the generalization bound of the method on
the test risk. With extensive experiments on synthetic and real datasets, we
demonstrate that the proposed method substantially improves over existing
alternatives in prediction accuracy and robustness on both regression and
classification tasks. We also assess its effectiveness on a user city
prediction dataset from a large technology company.