2 citations · 2 across the 5 of their papers we have counts for
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Generative modeling for the bootstrap
Leon Tran, Ting Ye, Peng Ding +1
Generative modeling builds on and substantially advances the classical idea of simulating synthetic data from observed samples. This paper shows that this principle is not only nat…
Quasi Instrumental Variable Methods for Stable Hidden Confounding and Binary Outcome
Zhonghua Liu, Baoluo Sun, Ting Ye +2
Instrumental variable (IV) methods are central to causal inference from observational data, particularly when a randomized experiment is not feasible. However, of the three convent…
A Bayesian Approach for Selecting Relevant External Data (BASE): Application to a study of Long-Term Outcomes in a Hemophilia Gene Therapy Trial
Tianyu Pan, Yiyao Shi, Xiang Zhang +2
Gene therapies aim to address the root causes of diseases, particularly those stemming from rare genetic defects that can be life-threatening or severely debilitating. Although an…
Semiparametric causal mediation analysis with unmeasured mediator-outcome confounding
BaoLuo Sun, Ting Ye
Although the exposure can be randomly assigned in studies of mediation effects, any form of direct intervention on the mediator is often infeasible. As a result, unmeasured mediato…
On Mendelian Randomization Mixed-Scale Treatment Effect Robust Identification (MR MiSTERI) and Estimation for Causal Inference
Zhonghua Liu, Ting Ye, Baoluo Sun +2
Standard Mendelian randomization analysis can produce biased results if the genetic variant defining the instrumental variable (IV) is confounded and/or has a horizontal pleiotropi…