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AteMeVs

Average Treatment Effects with Measurement Error and Variable Selection for Confounders

v0.1.0 · Sep 4, 2023 · GPL-2

Description

A recent method proposed by Yi and Chen (2023) <doi:10.1177/09622802221146308> is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies MASS ncvreg AteMeVs

Version History

1 tracked
new 0.1.0 Mar 10, 2026

R Observatory began tracking this package on Mar 10, 2026; it first appeared on CRAN Sep 4, 2023. Releases before tracking aren’t shown.