310 citations · 2k across the 67 of their papers we have counts for
8 papers · 1 filter
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
Haoyu Chen, Wenbin Lu, Rui Song +1
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings th…
CAPITAL: Optimal Subgroup Identification via Constrained Policy Tree Search
Hengrui Cai, Wenbin Lu, Rachel Marceau West +2
Personalized medicine, a paradigm of medicine tailored to a patient's characteristics, is an increasingly attractive field in health care. An important goal of personalized medicin…
Statistical Inference for Online Decision Making via Stochastic Gradient Descent
Haoyu Chen, Wenbin Lu, Rui Song
Online decision making aims to learn the optimal decision rule by making personalized decisions and updating the decision rule recursively. It has become easier than before with th…
Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting
Haoyu Chen, Wenbin Lu, Rui Song
Online decision-making problem requires us to make a sequence of decisions based on incremental information. Common solutions often need to learn a reward model of different action…
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
Hengrui Cai, Chengchun Shi, Rui Song +1
We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment dec…
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
Chengchun Shi, Runzhe Wan, Rui Song +2
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this paper, we propose a novel Forward-Backward Learning procedure to test MA in s…
Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings
C. Shi, S. Zhang, W. Lu +1
Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision making problems. The goodness…
Robust Learning for Optimal Treatment Decision with NP-Dimensionality
Chengchun Shi, Rui Song, Wenbin Lu
In order to identify important variables that are involved in making optimal treatment decision, Lu et al. (2013) proposed a penalized least squared regression framework for a fixe…