UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
Average Causal Effect Estimation Allowing Covariate Measurement Error
Yi Huang, Xiaoyu Dong, Andrew Raim, Elande Baro,
Department of Mathematics and Statistics
Dr. Karen Bandeen-Roche, Johns Hopkins Bloomberg School of Public Health
Dr. Cunlin Wang, CDER, FDA
Covariates are often measured with error in biomedical and policy
studies, which is a violation of the strong ignorability assumption. The
naive approach is to ignore the error and use the observed covariates in
current propensity score framework for average causal effect (ACE)
estimation. However, after extending the existing causal framework
incorporating assumptions allowing errors-in-covariates, the naive
approach typically produces biased ACE inference. In this project, we
develop a finite mixture model framework for ACE estimation with
continuous outcomes, which captures the uncertainty in propensity score
subclassification from unobserved measurement error using the joint
likelihood. The proposed approach will estimate the propensity score
subgroup membership and subgroup-specific treatment effect jointly. This
will extend the current propensity score subclassification approach to
accommodate cases where covariates are measured with errors.