36 citations · 105 across the 17 of their papers we have counts for
18 papers
Privacy-preserving causal mediation analysis using distributed electronic health record networks
Hyojung Jang, Rotana Radwan, Malcolm Risk +5
Electronic health record (EHR) networks provide unprecedented opportunities to study treatment mechanisms at scale, but mediation analyses across institutions are often hindered by…
A Framework for Understanding Selection Bias in Real-World Healthcare Data
Ritoban Kundu, Xu Shi, Jean Morrison +2
Using administrative patient-care data such as Electronic Health Records (EHR) and medical/ pharmaceutical claims for population-based scientific research has become increasingly c…
The Effect of Alcohol intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data
Chixiang Chen, Shuo Chen, Zhenyao Ye +3
Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incomplete…
Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls
Erich Kummerfeld, Jaewon Lim, Xu Shi
Negative control variables are increasingly used to adjust for unmeasured confounding bias in causal inference using observational data. They are typically identified by subject ma…
Doubly Robust Proximal Synthetic Controls
Hongxiang Qiu, Xu Shi, Wang Miao +2
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treate…
Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
Kendrick Qijun Li, Xu Shi, Wang Miao +1
Unmeasured confounding and selection bias are often of concern in observational studies and may invalidate a causal analysis if not appropriately accounted for. Under outcome-depen…
Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness
Kendrick Qijun Li, Xu Shi, Wang Miao +1
The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as In…
Online Causal Inference with Application to Near Real-Time Post-Market Vaccine Safety Surveillance
Xu Shi, Lan Luo
Streaming data routinely generated by mobile phones, social networks, e-commerce, and electronic health records present new opportunities for near real-time surveillance of the imp…
Data Integration in Causal Inference
Xu Shi, Ziyang Pan, Wang Miao
Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in…
Proximal Causal Inference for Complex Longitudinal Studies
Andrew Ying, Wang Miao, Xu Shi +1
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata,…
Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework
Xu Shi, Kendrick Li, Wang Miao +2
Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units buil…
Semiparametric proximal causal inference
Yifan Cui, Hongming Pu, Xu Shi +2
Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchang…
Patient Recruitment Using Electronic Health Records Under Selection Bias: a Two-phase Sampling Framework
Guanghao Zhang, Lauren J. Beesley, Bhramar Mukherjee +1
Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort fr…
An Introduction to Proximal Causal Learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui +2
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects a…
A Selective Review of Negative Control Methods in Epidemiology
Xu Shi, Wang Miao, Eric Tchetgen Tchetgen
Purpose of Review: Negative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience an…
A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies
Eric J. Tchetgen Tchetgen, Xu Shi, Tamar Sofer +1
It is increasingly of interest in statistical genetics to test for the presence of a mechanistic interaction between genetic (G) and environmental (E) risk factors by testing for t…
Estimation of natural indirect effects robust to unmeasured confounding and mediator measurement error
Isabel R. Fulcher, Xu Shi, Eric J. Tchetgen Tchetgen
The use of causal mediation analysis to evaluate the pathways by which an exposure affects an outcome is widespread in the social and biomedical sciences. Recent advances in this a…
Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding
Xu Shi, Wang Miao, Jennifer C. Nelson +1
Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing…