131 citations · 352 across the 94 of their papers we have counts for
12 papers · 2 filters
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
Steven G. Xu, Shu Yang, Brian J. Reich
Standard causal inference characterizes treatment effect through averages, but the counterfactual distributions could be different in not only the central tendency but also spread…
Matching Estimators of Causal Effects in Clustered Observational Studies with Application to Quantifying the Impact of Marine Protected Areas on Biodiversity
Can Cui, Shu Yang, Brian J Reich +1
Marine conservation preserves fish biodiversity, protects marine and coastal ecosystems, and supports climate resilience and adaptation. Despite the importance of establishing mari…
Towards R-learner with Continuous Treatments
Yichi Zhang, Dehan Kong, Shu Yang
The R-learner is widely used in causal inference due to its flexibility and efficiency in estimating the conditional average treatment effect. However, extending the R-learner fram…
Soft calibration for selection bias problems under mixed-effects models
Chenyin Gao, Shu Yang, Jae Kwang Kim
Calibration weighting has been widely used to correct selection biases in non-probability sampling, missing data, and causal inference. The main idea is to calibrate the biased sam…
Semi-Parametric Sensitivity Analysis for Trials with Irregular and Informative Assessment Times
Bonnie B. Smith, Yujing Gao, Shu Yang +3
Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, thi…
Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Study
Xiaoqing Tan, Shu Yang, Wenyu Ye +3
Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have b…
Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes
Siyi Liu, Yilong Zhang, Gregory T Golm +3
Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conven…
Sensitivity analysis in longitudinal clinical trials via distributional imputation
Siyi Liu, Shu Yang, Yilong Zhang +2
Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empiric…
Functional principal component analysis with informative observation times
Peijun Sang, Dehan Kong, Shu Yang
Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serves as important building blocks for foreca…
Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling
Chenyin Gao, Katherine Jenny Thompson, Shu Yang +1
Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbo…
Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population
Dasom Lee, Shu Yang, Xiaofei Wang
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often…
Targeted Optimal Treatment Regime Learning Using Summary Statistics
Jianing Chu, Wenbin Lu, Shu Yang
Personalized decision-making, aiming to derive optimal treatment regimes based on individual characteristics, has recently attracted increasing attention in many fields, such as me…