36 citations · 44 across the 9 of their papers we have counts for
5 papers · 1 filter
Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates
Ruoyu Wang, Qihua Wang, Wang Miao +1
In a completely randomized experiment, the variances of treatment effect estimators in the finite population are usually not identifiable and hence not estimable. Although some est…
Semiparametric proximal causal inference
Yifan Cui, Hongming Pu, Xu Shi +2
Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchang…
Improving efficiency of inference in clinical trials with external control data
Xinyu Li, Wang Miao, Fang Lu +1
Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are…
Identifying effects of multiple treatments in the presence of unmeasured confounding
Wang Miao, Wenjie Hu, Elizabeth L. Ogburn +1
Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured con…
An Introduction to Proximal Causal Learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui +2
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects a…