15 citations · 23 across the 6 of their papers we have counts for
7 papers · 1 filter
A unified analysis of regression adjustment in randomized experiments
Katarzyna Reluga, Ting Ye, Qingyuan Zhao
Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has s…
Structural mean models for instrumented difference-in-differences
Tat-Thang Vo, Ting Ye, Ashkan Ertefaie +5
In the standard difference-in-differences research design, the parallel trends assumption may be violated when the relationship between the exposure trend and the outcome trend is…
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data
Sai Li, Ting Ye
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Alth…
Minimax Rates and Adaptivity in Combining Experimental and Observational Data
Shuxiao Chen, Bo Zhang, Ting Ye
Randomized controlled trials (RCTs) are the gold standard for evaluating the causal effect of a treatment; however, they often have limited sample sizes and sometimes poor generali…
Instrumented Difference-in-Differences
Ting Ye, Ashkan Ertefaie, James Flory +2
Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variabl…
Inference on Average Treatment Effect under Minimization and Other Covariate-Adaptive Randomization Methods
Ting Ye, Yanyao Yi, Jun Shao
Covariate-adaptive randomization schemes such as the minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across progno…