Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Pimentel, Samuel"'
Disparities in health or well-being experienced by minority groups can be difficult to study using the traditional exposure-outcome paradigm in causal inference, since potential outcomes in variables such as race or sexual minority status are challen
Externí odkaz:
http://arxiv.org/abs/2407.00139
Autor:
Han, Shichao, Pimentel, Samuel D.
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 credible causal inferences. The problem of constructing matched sets ma
Externí odkaz:
http://arxiv.org/abs/2406.18819
Autor:
Pimentel, Samuel D., Yu, Ruoqi
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 controls. After matching, inference is often conducted using methods
Externí odkaz:
http://arxiv.org/abs/2403.01330
Sensitivity to unmeasured confounding is not typically a primary consideration in designing treated-control comparisons in observational studies. We introduce a framework allowing researchers to optimize robustness to omitted variable bias at the des
Externí odkaz:
http://arxiv.org/abs/2307.00093
Autor:
Liao, Lauren D., Pimentel, Samuel D.
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 not guaranteed. Investigators must balance background variables so the
Externí odkaz:
http://arxiv.org/abs/2302.10367
Publikováno v:
The American Statistician, 2024, p. 1-17
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variab
Externí odkaz:
http://arxiv.org/abs/2301.09754
Autor:
Huang, Melody, Pimentel, Samuel D.
Weighting methods are popular tools for estimating causal effects; assessing their robustness under unobserved confounding is important in practice. In the following paper, we introduce a new set of sensitivity models called "variance-based sensitivi
Externí odkaz:
http://arxiv.org/abs/2208.01691
Autor:
Pimentel, Samuel D., Huang, Yaxuan
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 using this distribution as the basis for inference. This approach ignores
Externí odkaz:
http://arxiv.org/abs/2207.05019
Autor:
Glazer, Amanda K., Pimentel, Samuel D.
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, control units each have many possible comparison, or "pseudo-treatment," tim
Externí odkaz:
http://arxiv.org/abs/2205.01061
Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for bal
Externí odkaz:
http://arxiv.org/abs/2102.13218