23 citations · 25 across the 3 of their papers we have counts for
4 papers
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
Patrick Vossler, Nathan Justin, Sina Aghaei +3
ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art…
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo, Bill Tang, Kathryn Dullerud +3
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproporti…
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algo…
Learning Optimal Prescriptive Trees from Observational Data
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observati…