2 citations · 3 across the 5 of their papers we have counts for
6 papers
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…
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…
Generalized Fitted Q-Iteration with Clustered Data
Liyuan Hu, Jitao Wang, Zhenke Wu +1
This paper focuses on reinforcement learning (RL) with clustered data, which is commonly encountered in healthcare applications. We propose a generalized fitted Q-iteration (FQI) a…
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…
Multivariate Dynamic Mediation Analysis under a Reinforcement Learning Framework
Lan Luo, Chengchun Shi, Jitao Wang +2
Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there a…
A Reinforcement Learning Framework for Dynamic Mediation Analysis
Lin Ge, Jitao Wang, Chengchun Shi +2
Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes and receives increasing attention in various scientific domains to el…