78 citations · 86 across the 8 of their papers we have counts for
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stat.ME2020
A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data
Liangyuan Hu, Jungang Zou, Chenyang Gu +3
In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorabili…
stat.AP2020★ 78 cited
Estimating heterogeneous survival treatment effect in observational data using machine learning
Liangyuan Hu, Jiayi Ji, Fan Li
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with surviva…
stat.ME2020★ 4 cited
Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome
Liangyuan Hu, Chenyang Gu, Michael Lopez +2
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and…