High-Performance Causal Inference for COVID-19 Mitigation and Response

This project will develop a scalable computational approach to conducting causal inference on massive COVID-19 observational datasets. For COVID-19 mitigation and response studies, the ability to conduct randomized control trials (RCT) would be invaluable, but is, unfortunately, not possible since we cannot randomly choose who is infected with the virus nor can we randomize who receives essential care. An alternative route toward the same end is to employ causal inference models with the observational COVID-19 data that become increasingly available for public access as desensitized clinical records. Through our previous work, theoretical statistical foundations have been established and a parallel computing algorithm based on the message-passing interface (MPI), called BOSS, has been implemented to compute optimal subsets that are statistically indistinguishable from the treatment group observations. In this project, we propose to enhance the BOSS model as a hybrid computational model that is able to leverage data-intensive computing paradigms and GPU-accelerated high-performance computing (HPC).