20 citations · 32 across the 10 of their papers we have counts for
11 papers
MultiObjMatch: Matching with Optimal Tradeoffs between Multiple Objectives in R
Shichao Han, Samuel D. Pimentel
In an observational study, matching aims to create many small sets of similar treated and control units from initial samples that may differ substantially in order to permit more c…
Re-evaluating the impact of hormone replacement therapy on heart disease using match-adaptive randomization inference
Samuel D. Pimentel, Ruoqi Yu
Matching is an appealing way to design observational studies because it mimics the data structure produced by stratified randomized trials, pairing treated individuals with similar…
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…
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…
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…
Variance-based sensitivity analysis for weighting estimators result in more informative bounds
Melody Huang, Samuel D. Pimentel
Weighting methods are popular tools for estimating causal effects; assessing their robustness under unobserved confounding is important in practice. In the following paper, we intr…
Covariate-adaptive randomization inference in matched designs
Samuel D. Pimentel, Yaxuan Huang
It is common to conduct causal inference in matched observational studies by proceeding as though treatment assignments within matched sets are assigned uniformly at random and usi…
Robust inference for matching under rolling enrollment
Amanda K. Glazer, Samuel D. Pimentel
Matching in observational studies faces complications when units enroll in treatment on a rolling basis. While each treated unit has a specific time of entry into the study, contro…
Interpretable Sensitivity Analysis for Balancing Weights
Dan Soriano, Eli Ben-Michael, Peter J. Bickel +2
Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measur…
The uniform general signed rank test and its design sensitivity
Steven R. Howard, Samuel D. Pimentel
A sensitivity analysis in an observational study tests whether the qualitative conclusions of an analysis would change if we were to allow for the possibility of limited bias due t…
Variable-Ratio Matching with Fine Balance in a Study of Peer Health Exchange
Luke Keele, Sam Pimentel, Frank Yoon
In observational studies of treatment effects, matched samples are created so treated and control groups are similar in terms of observable covariates. Traditionally such matched s…