CHEMIST_package {CHEMIST}R Documentation

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


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.




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.



[Package CHEMIST version 0.1.5 Index]