45 citations · 111 across the 31 of their papers we have counts for
32 papers
Instrumental Variable Analysis in Underrepresented Subpopulations Powered by Knowledge Transfer
Ruoyu Wang, Zijian Xu, Zijian Guo +3
Instrumental variable (IV) analysis in underrepresented target populations suffers from low statistical efficiency and weak IV bias due to limited sample size. Leveraging external…
Identification and Robust Inference for Multiple Treatments with Possibly Invalid Instruments
Ziwei Mei, Qingliang Fan, Zijian Guo
The instrumental variable (IV) method is widely used to infer causal effects in observational studies with unmeasured confounding, but invalid instruments can compromise both popul…
Guided Adversarial Robust Transfer Learning with Source Mixing
Xin Xiong, Zijian Guo, Tianxi Cai
Transfer learning is a critical technique that enables the application of knowledge gained from existing tasks or domains to improve performance on a new one, reducing the need for…
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Zhenyu Wang, Peter Bühlmann, Zijian Guo
Empirical risk minimization often performs poorly when the distribution of the target domain differs from those of source domains. To address such potential distribution shifts, we…
TSCI: two stage curvature identification for causal inference with invalid instruments
David Carl, Corinne Emmenegger, Peter Bühlmann +1
TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches…
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression
Yue Liu, Molei Liu, Zijian Guo +1
Surrogate variables play an important role in various fields due to the scarcity or absence of gold standard labels. We develop a novel approach named SASH for Surrogate-Assisted a…
Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models
T. Tony Cai, Zijian Guo, Yin Xia
This paper presents a selective survey of recent developments in statistical inference and multiple testing for high-dimensional regression models, including linear and logistic re…
Robust Inference for Federated Meta-Learning
Zijian Guo, Xiudi Li, Larry Han +1
Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogene…
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
Taehyeon Koo, Youjin Lee, Dylan S. Small +1
We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It im…
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
Qingliang Fan, Zijian Guo, Ziwei Mei
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of c…
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
Zijian Guo, Mengchu Zheng, Peter Bühlmann
We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (TSCI)…
SIHR: Statistical Inference in High-Dimensional Linear and Logistic Regression Models
Prabrisha Rakshit, Zhenyu Wang, T. Tony Cai +1
We introduce the R package \CRANpkg{SIHR} for statistical inference in high-dimensional generalized linear models with continuous and binary outcomes. The package provides function…
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
Jue Hou, Zijian Guo, Tianxi Cai
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we d…
Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling
Zijian Guo
Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid ins…
Inference for the Case Probability in High-dimensional Logistic Regression
Zijian Guo, Prabrisha Rakshit, Daniel S. Herman +1
Labeling patients in electronic health records with respect to their statuses of having a disease or condition, i.e. case or control statuses, has increasingly relied on prediction…
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies
Zijian Guo
Integrative analysis of data from multiple sources is critical to making generalizable discoveries. Associations that are consistently observed across multiple source populations a…
Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables
Sai Li, Zijian Guo
Instrumental variable methods are widely used for inferring the causal effect in the presence of unmeasured confounders. Existing instrumental variable methods for nonlinear outcom…
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Zijian Guo, Domagoj Ćevid, Peter Bühlmann
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding. We focus on a high-dimensional linear regr…
Group Inference in High Dimensions with Applications to Hierarchical Testing
Zijian Guo, Claude Renaux, Peter Bühlmann +1
High-dimensional group inference is an essential part of statistical methods for analysing complex data sets, including hierarchical testing, tests of interaction, detection of het…
Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models
Zijian Guo, Wei Yuan, Cun-Hui Zhang
Additive models play an essential role in studying non-linear relationships. Despite many recent advances in estimation, there is a lack of methods and theories for inference in hi…
Extreme Eigenvalues of Nonlinear Correlation Matrices with Applications to Additive Models
Zijian Guo, Cun-Hui Zhang
The maximum correlation of functions of a pair of random variables is an important measure of stochastic dependence. It is known that this maximum nonlinear correlation is identica…
Optimal Statistical Inference for Individualized Treatment Effects in High-dimensional Models
Tianxi Cai, Tony Cai, Zijian Guo
The ability to predict individualized treatment effects (ITEs) based on a given patient's profile is essential for personalized medicine. We propose a hypothesis testing approach t…
Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applications
T. Tony Cai, Zijian Guo
This paper considers statistical inference for the explained variance under the high-dimensional linear model in the semi-supervised setting, where i…
Testing Endogeneity with High Dimensional Covariates
Zijian Guo, Hyunseung Kang, T. Tony Cai +1
Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous variables and putting lit…