124 citations · 198 across the 13 of their papers we have counts for
13 papers
The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
Eileanor LaRocco, Sarah Tan, Adarsh Subbaswamy +4
Autonomous AI systems are transitioning from advisory roles to autonomous ones for medication prescriptions. Recent U.S. bill H.R. 238 and Utah's prescription-renewal pilot program…
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Zhi Chen, Sarah Tan, Urszula Chajewska +2
Missing values are a fundamental problem in data science. Many datasets have missing values that must be properly handled because the way missing values are treated can have large…
Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham +9
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment. However, standard e…
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
Leon Yao, Caroline Lo, Israel Nir +4
Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outc…
Interpretable Personalized Experimentation
Han Wu, Sarah Tan, Weiwei Li +7
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they…
How Interpretable and Trustworthy are GAMs?
Chun-Hao Chang, Sarah Tan, Ben Lengerich +2
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning. However, there are many algorithms for training GAMs, and these can learn di…