DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

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

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