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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.5 |
2026-04-09 windows/windows R-4.5 | CHEMIST_0.1.5.zip |
60.9 KiB |