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.
| Version: |
0.1.5 |
| Depends: |
R (≥ 3.3.1), MASS |
| Imports: |
stats, XICOR, LaplacesDemon |
| Published: |
2023-05-01 |
| Author: |
Wei-Hsin Hsu [aut, cre],
Li-Pang Chen [aut] |
| Maintainer: |
Wei-Hsin Hsu <anson60214 at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
CHEMIST results |
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