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stat.ML2025
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal +12
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal pe…
stat.ML2018
Interpretable Almost Matching Exactly for Causal Inference
Yameng Liu, Aw Dieng, Sudeepa Roy +2
We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social…