42 citations · 95 across the 11 of their papers we have counts for
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stat.ME2023★ 9 cited
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan +2
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be in…
stat.ME2020★ 3 cited
Stable discovery of interpretable subgroups via calibration in causal studies
Raaz Dwivedi, Yan Shuo Tan, Briton Park +4
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC…