R/CHEMIST-package.R

In CHEMIST: Causal Inference with High-Dimensional Error-Prone Covariates and Misclassified Treatments

#' @title Causal Inference with High-Dimensional Error-Prone Covariates and
#' Misclassified Treatments
#'
#' @description The package CHEMIST, referred to Causal inference with High-dimsensional Error-
#' prone Covariates and MISclassified Treatments, aims to deal with the average
#' treatment effect (ATE), where the data are subject to high-dimensionality and
#' measurement error. This package primarily contains two functions: one is
#' Data_Gen that is applied to generate artificial data, including potential
#' outcomes, error-prone treatments and covariates, and the other is FATE that is
#' used to estimate ATE with measurement error correction.

#' @details This package aims to estimate ATE in the presence of high-dimensional and
#' error-prone data. The strategy is to do variable selection by feature screening
#' and general outcome-adaptive lasso. After that, measurement error in
#' covariates are corrected. Finally, with informative and error corrected data
#' obtained, the propensity score can be estimated and can be used to estimate
#' ATE by the inverse probability weight approach.
#'
#' @return CHEMIST_package
#'
#' @export

CHEMIST_package<-function(){return(CHEMIST_package="CHEMIST_package") }