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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.
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Package details |
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| Author | Wei-Hsin Hsu [aut, cre], Li-Pang Chen [aut] |
| Maintainer | Wei-Hsin Hsu <anson60214@gmail.com> |
| License | GPL-3 |
| Version | 0.1.5 |
| Package repository | View on CRAN |
| Installation |
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