Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs)
from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with
treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements
the conformal prediction framework that enables computation and illustration of conformal prediction (CP)
intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional
functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with
treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks.
| Version: |
0.0.107 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
ROCR, caret, neuralnet, SuperLearner, ggplot2, tidyr, magrittr, reticulate, keras3, Hmisc |
| Suggests: |
testthat (≥ 3.0.0), dplyr, class, xgboost, randomForest, glmnet, ranger, gam, e1071, gbm, tensorflow |
| Published: |
2025-10-30 |
| DOI: |
10.32614/CRAN.package.DeepLearningCausal |
| Author: |
Nguyen K. Huynh 
[aut, cre],
Bumba Mukherjee
[aut],
Yang Yang [aut] |
| Maintainer: |
Nguyen K. Huynh <khoinguyen.huynh at r.hit-u.ac.jp> |
| BugReports: |
https://github.com/hknd23/DeepLearningCausal/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/hknd23/DeepLearningCausal |
| NeedsCompilation: |
no |
| CRAN checks: |
DeepLearningCausal results |