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ATE.ERROR  

Estimating ATE with Misclassified Outcomes and Mismeasured Covariates
View on CRAN: Click here


Download and install ATE.ERROR package within the R console
Install from CRAN:
install.packages("ATE.ERROR")

Install from Github:
library("remotes")
install_github("cran/ATE.ERROR")

Install by package version:
library("remotes")
install_version("ATE.ERROR", "1.0.0")



Attach the package and use:
library("ATE.ERROR")
Maintained by
Aryan Rezanezhad
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-09-10
Latest Update: 2024-09-10
Description:
Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.
How to cite:
Aryan Rezanezhad (2024). ATE.ERROR: Estimating ATE with Misclassified Outcomes and Mismeasured Covariates. R package version 1.0.0, https://cran.r-project.org/web/packages/ATE.ERROR. Accessed 07 Nov. 2024.
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