2 citations · 2 across the 3 of their papers we have counts for
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stat.ME2025
Generalized coarsened confounding for causal effects: a large-sample framework
Debashis Ghosh, Lei Wang
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a clas…
stat.ME2024
A Surrogate Endpoint Based Provisional Approval Causal Roadmap
Peter B. Gilbert, James Peng, Larry Han +12
For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide dir…
stat.ME2023★ 2 cited
Ultra-high dimensional confounder selection algorithms comparison with application to radiomics data
Ismaïla Baldé, Debashis Ghosh
Radiomics is an emerging area of medical imaging data analysis particularly for cancer. It involves the conversion of digital medical images into mineable ultra-high dimensional da…