20 citations · 32 across the 10 of their papers we have counts for
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stat.ME2023★ 3 cited
Design Sensitivity and Its Implications for Weighted Observational Studies
Melody Huang, Dan Soriano, Samuel D. Pimentel
Sensitivity to unmeasured confounding is not typically a primary consideration in designing treated-control comparisons in observational studies. We introduce a framework allowing…
stat.ME2023
JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R
Lauren D. Liao, Samuel D. Pimentel
Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is…
stat.ME2023★ 4 cited
Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot
Lauren D. Liao, Yeyi Zhu, Amanda L. Ngo +2
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measur…