6 papers
Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
Yuqi Li, Quinn Lanners, Matthew M. Engelhard
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a…
Interval-Based AUC (iAUC): Extending ROC Analysis to Uncertainty-Aware Classification
Yuqi Li, Matthew M. Engelhard
In high-stakes risk prediction, quantifying uncertainty through interval-valued predictions is essential for reliable decision-making. However, standard evaluation tools like the r…
Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
Minghui Sun, Matthew M. Engelhard, Benjamin A. Goldstein
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visit…
FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
Pengxi Liu, Yi Shen, Matthew M. Engelhard +4
Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and…
CLEAR: Unlearning Spurious Style-Content Associations with Contrastive LEarning with Anti-contrastive Regularization
Minghui Sun, Benjamin A. Goldstein, Matthew M. Engelhard
Learning representations unaffected by superficial characteristics is important to ensure that shifts in these characteristics at test time do not compromise downstream prediction…
Infinite hierarchical contrastive clustering for personal digital envirotyping
Ya-Yun Huang, Joseph McClernon, Jason A. Oliver +1
Daily environments have profound influence on our health and behavior. Recent work has shown that digital envirotyping, where computer vision is applied to images of daily environm…