31 citations · 116 across the 22 of their papers we have counts for
7 papers · 1 filter
Social Distancing and COVID-19: Randomization Inference for a Structured Dose-Response Relationship
Bo Zhang, Siyu Heng, Ting Ye +1
Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much…
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…
Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
Ting Ye, Jun Shao, Yanyao Yi +1
In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model…
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…
Bridging preference-based instrumental variable studies and cluster-randomized encouragement experiments: study design, noncompliance, and average cluster effect ratio
Bo Zhang, Siyu Heng, Emily J. MacKay +1
Instrumental variable methods are widely used in medical and social science research to draw causal conclusions when the treatment and outcome are confounded by unmeasured confound…
A Negative Correlation Strategy for Bracketing in Difference-in-Differences
Ting Ye, Luke Keele, Raiden Hasegawa +1
The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison…