4 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
A decorrelation method for general regression adjustment in randomized experiments
Fangzhou Su, Wenlong Mou, Peng Ding +1
We study regression adjustment with general function class approximations for estimating the average treatment effect in the design-based setting. Standard regression adjustment in…
Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding
Douglas Faries, Chenyin Gao, Xiang Zhang +7
The assumption of no unmeasured confounders is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of re…
A randomization-based theory for preliminary testing of covariate balance in controlled trials
Anqi Zhao, Peng Ding
Randomized trials balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized tr…
When is the estimated propensity score better? High-dimensional analysis and bias correction
Fangzhou Su, Wenlong Mou, Peng Ding +1
Anecdotally, using an estimated propensity score is superior to the true propensity score in estimating the average treatment effect based on observational data. However, this clai…
Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency
Wenlong Mou, Peng Ding, Martin J. Wainwright +1
We study optimal procedures for estimating a linear functional based on observational data. In many problems of this kind, a widely used assumption is strict overlap, i.e., uniform…