550 citations · 1k across the 19 of their papers we have counts for
6 papers · 1 filter
Confounding Feature Acquisition for Causal Effect Estimation
Shirly Wang, Seung Eun Yi, Shalmali Joshi +1
Reliable treatment effect estimation from observational data depends on the availability of all confounding information. While much work has targeted treatment effect estimation fr…
Ethical Machine Learning in Health Care
Irene Y. Chen, Emma Pierson, Sherri Rose +3
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical consideratio…
Probabilistic Machine Learning for Healthcare
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi +1
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review,…
Sequential Explanations with Mental Model-Based Policies
Arnold YS Yeung, Shalmali Joshi, Joseph Jay Williams +1
The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this inf…
Counterfactually Guided Off-policy Transfer in Clinical Settings
Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi
Domain shift, encountered when using a trained model for a new patient population, creates significant challenges for sequential decision making in healthcare since the target doma…
What went wrong and when? Instance-wise Feature Importance for Time-series Models
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell +2
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature. We propose FIT, a fram…