Causal Inference with High-Dimensional Error-Prone Covariates
and Misclassified Treatments
Description
We aim 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, which are used to generate artificial data and estimate ATE with high-dimensional
and error-prone data accommodated.