Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to
measure and achieve fair calibration is with multicalibration. Multicalibration constrains …

Omnipredictors are simple prediction functions that encode loss-minimizing predictions with
respect to a hypothesis class $\H $, simultaneously for every loss function within a class of …

Multicalibration is a desirable fairness criteria that constrains calibration error among flexibly-
defined groups in the data while maintaining overall calibration. However, when outcome …

Estimating heterogeneous treatment effects is important to tailor treatments to those
individuals who would most likely benefit. However, conditional average treatment effect …

Traditional statistical and machine learning methods assume identical distribution for the
training and test data sets. This assumption, however, is often violated in real applications …

Background: Fair clinical prediction models are crucial for achieving equitable health
outcomes. Recently, intersectionality has been applied to develop fairness algorithms that …

Machine learning artifacts are increasingly embedded in society, often in the form of
automated decision-making processes. One major reason for this, along with …

Universal Adaptability Page 1 Universal Adaptability A New Method to Draw Inference from
Non-Probability Surveys and Other Data Sources Christoph Kern Department of Statistics …

When algorithmic predictions inform social decision-making, these predictions don't just
forecast the world around them: they actively shape it. Building models that influence the …

Multi-calibration (Hebert-Johnson et al., 2018; Kim et al., 2019) ensures that predictions are
unbiased across every (weighted) subpopulation defined by c∈ C We derive a direct …