31 citations · 143 across the 28 of their papers we have counts for
28 papers
Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
Yuhao Deng, Haoyu Wei, Donglin Zeng +2
During clinical trials evaluating a drug's effect on a survival endpoint, intermediate events often occur in addition to the primary event. The treatment can exert its effect on th…
Multiplier Bootstrap-based Exploration
Runzhe Wan, Haoyu Wei, Branislav Kveton +1
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncerta…
Deep Spectral Q-learning with Application to Mobile Health
Yuhe Gao, Chengchun Shi, Rui Song
Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applic…
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Yang Xu, Chengchun Shi, Shikai Luo +2
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of…
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Yang Xu, Jin Zhu, Chengchun Shi +2
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior polic…
Sure Screening for Transelliptical Graphical Models
Yuxiang Xie, Chengchun Shi, Rui Song
We propose a sure screening approach for recovering the structure of a transelliptical graphical model in the high dimensional setting. We estimate the partial correlation graph by…
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…
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
Chengchun Shi, Shikai Luo, Yuan Le +2
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimizati…
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
Chengchun Shi, Jin Zhu, Ye Shen +3
This paper is concerned with constructing a confidence interval for a target policy's value offline based on a pre-collected observational data in infinite horizon settings. Most o…
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
Chengchun Shi, Runzhe Wan, Ge Song +3
The two-sided markets such as ride-sharing companies often involve a group of subjects who are making sequential decisions across time and/or location. With the rapid development o…
Jump Interval-Learning for Individualized Decision Making
Hengrui Cai, Chengchun Shi, Rui Song +1
An individualized decision rule (IDR) is a decision function that assigns each individual a given treatment based on his/her observed characteristics. Most of the existing works in…
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Runzhe Wan, Sheng Zhang, Chengchun Shi +2
Order dispatch is one of the central problems to ride-sharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance on this problem. H…
Deeply-Debiased Off-Policy Interval Estimation
Chengchun Shi, Runzhe Wan, Victor Chernozhukov +1
Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy. In addition to a point estimate, many applications would be…
GEAR: On Optimal Decision Making with Auxiliary Data
Hengrui Cai, Rui Song, Wenbin Lu
Personalized optimal decision making, finding the optimal decision rule (ODR) based on individual characteristics, has attracted increasing attention recently in many fields, such…
Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome
Hengrui Cai, Wenbin Lu, Rui Song
We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available. The outcome of interest is limited in the sense that it i…
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…
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Runzhe Wan, Xinyu Zhang, Rui Song
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders…
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health
Liangyu Zhu, Wenbin Lu, Rui Song
In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime that optimi…
Kernel Assisted Learning for Personalized Dose Finding
Liangyu Zhu, Wenbin Lu, Michael R. Kosorok +1
An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such as physical conditions, genetic factors and medicati…
A New Framework for Online Testing of Heterogeneous Treatment Effect
Miao Yu, Wenbin Lu, Rui Song
We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under contin…
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…
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Chengchun Shi, Xiaoyu Wang, Shikai Luo +3
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major chall…