430 citations · 856 across the 28 of their papers we have counts for
7 papers · 1 filter
Adapting to Latent Subgroup Shifts via Concepts and Proxies
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour +7
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When thi…
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations
Qingyao Sun, Kevin Murphy, Sayna Ebrahimi +1
Changes in the data distribution at test time can have deleterious effects on the performance of predictive models . We consider situations where there are additional meta-…
Fairness and robustness in anti-causal prediction
Maggie Makar, Alexander D'Amour
Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem rel…
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
Jiajing Zheng, Jiaxi Wu, Alexander D'Amour +1
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-o…
Boosting the interpretability of clinical risk scores with intervention predictions
Eric Loreaux, Ke Yu, Jonas Kemp +8
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…
Understanding the Risks and Rewards of Combining Unbiased and Possibly Biased Estimators, with Applications to Causal Inference
Michael Oberst, Alexander D'Amour, Minmin Chen +3
Several problems in statistics involve the combination of high-variance unbiased estimators with low-variance estimators that are only unbiased under strong assumptions. A notable…