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A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Jianhan Zhang, Jitao Wang, John D. Piette +3
Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcar…
Spatially Robust Inference with Predicted and Missing at Random Labels
Stephen Salerno, Zhenke Wu, Tyler McCormick
When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has c…
PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Jianhan Zhang, Jitao Wang, Chengchun Shi +3
Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment ac…
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
Jitao Wang, Chengchun Shi, John D. Piette +3
When applied in healthcare, reinforcement learning (RL) seeks to dynamically match the right interventions to subjects to maximize population benefit. However, the learned policy m…