46 citations · 118 across the 18 of their papers we have counts for
20 papers
Sequential Treatment Effect Estimation with Unmeasured Confounders
Yingrong Wang, Anpeng Wu, Baohong Li +4
This paper studies the cumulative causal effects of sequential treatments in the presence of unmeasured confounders. It is a critical issue in sequential decision-making scenarios…
Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +4
In causal inference, encouragement designs (EDs) are widely used to analyze causal effects, when randomized controlled trials (RCTs) are impractical or compliance to treatment cann…
Causal Inference with Complex Treatments: A Survey
Yingrong Wang, Haoxuan Li, Minqin Zhu +4
Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is…
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Minqin Zhu, Anpeng Wu, Haoxuan Li +8
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent stu…
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
Yingrong Wang, Anpeng Wu, Haoxuan Li +5
This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and lon…
Target PCA: Transfer Learning Large Dimensional Panel Data
Junting Duan, Markus Pelger, Ruoxuan Xiong
This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data…
Instrumental Variables in Causal Inference and Machine Learning: A Survey
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +1
Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. T…
Confounder Balancing for Instrumental Variable Regression with Latent Variable
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +2
This paper studies the confounding effects from the unmeasured confounders and the imbalance of observed confounders in IV regression and aims at unbiased causal effect estimation.…
Learning Individual Treatment Effects under Heterogeneous Interference in Networks
Ziyu Zhao, Yuqi Bai, Kun Kuang +2
Estimates of individual treatment effects from networked observational data are attracting increasing attention these days. One major challenge in network scenarios is the violatio…
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +6
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each sour…
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong +5
Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representation…
Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference
Zhaonan Qu, Ruoxuan Xiong, Jizhou Liu +1
In many observational studies in social science and medicine, subjects or units are connected, and one unit's treatment and attributes may affect another's treatment and outcome, v…
Federated Causal Inference in Heterogeneous Observational Data
Ruoxuan Xiong, Allison Koenecke, Michael Powell +3
We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individu…
Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection
Allison Koenecke, Michael Powell, Ruoxuan Xiong +22
In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory di…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training dat…
Optimal Experimental Design for Staggered Rollouts
Ruoxuan Xiong, Susan Athey, Mohsen Bayati +1
In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods where the starting time of the treatment may vary by unit. The…
Stable Prediction across Unknown Environments
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier wi…
State-Varying Factor Models of Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state…
Interpretable Sparse Proximate Factors for Large Dimensions
Markus Pelger, Ruoxuan Xiong
This paper proposes sparse and easy-to-interpret proximate factors to approximate statistical latent factors. Latent factors in a large-dimensional factor model can be estimated by…
Deep Learning Stock Volatility with Google Domestic Trends
Ruoxuan Xiong, Eric P. Nichols, Yuan Shen
We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors…