13 citations · 31 across the 13 of their papers we have counts for
6 papers · 1 filter
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects
Larry Han, Jue Hou, Kelly Cho +2
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecific…
Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response
Jue Hou, Rajarshi Mukherjee, Tianxi Cai
A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect assessment is the lack of precise information on important clinical variables, including the…
Semi-supervised Approach to Event Time Annotation Using Longitudinal Electronic Health Records
Liang Liang, Jue Hou, Hajime Uno +3
Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to…
Implicit Profiling Estimation for Semiparametric Models with Bundled Parameters
Yucong Lin, Jinhua Su, Yang Liu +2
Solving semiparametric models can be computationally challenging because the dimension of parameter space may grow large with increasing sample size. Classical Newton's method beco…
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…
Risk Prediction with Imperfect Survival Outcome Information from Electronic Health Records
Stephanie F. Chan, Jue Hou, Xuan Wang +1
Readily available proxies for time of disease onset such as time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor pro…