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