46 citations · 118 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…