AteMeVs - Average Treatment Effects with Measurement Error and Variable
Selection for Confounders
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.
