22 citations · 73 across the 12 of their papers we have counts for
3 papers · 1 filter
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Tomas M. Bosschieter, Zifei Xu, Hui Lan +5
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and ba…
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori +6
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed. However,…
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff +3
Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a gener…