Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in KC<nzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.
Version: 0.0.106 Depends: R (≥ 4.1.0) Imports: ROCR, caret, neuralnet, SuperLearner, class, xgboost, randomForest, glmnet, gam, e1071, gbm, Hmisc, ggplot2, dplyr, tidyr, magrittr, weights Suggests: testthat Published: 2025-06-11 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=DeepLearningCausal to link to this page.
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