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DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

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 Künzel 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: 0.0.104
Depends: R (≥ 4.1.0)
Imports: ROCR, caret, neuralnet, SuperLearner, class, xgboost, randomForest, glmnet, gam, e1071, gbm, Hmisc, weights
Suggests: testthat, ggplot2, tidyr, dplyr
Published: 2024-07-30
DOI: 10.32614/CRAN.package.DeepLearningCausal
Author: Nguyen K. Huynh ORCID iD [aut, cre], Bumba Mukherjee ORCID iD [aut], Irvin (Chen-Yu) Lee ORCID iD [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:

Reference manual: DeepLearningCausal.pdf

Downloads:

Package source: DeepLearningCausal_0.0.104.tar.gz
Windows binaries: r-devel: DeepLearningCausal_0.0.104.zip, r-release: DeepLearningCausal_0.0.104.zip, r-oldrel: DeepLearningCausal_0.0.104.zip
macOS binaries: r-release (arm64): DeepLearningCausal_0.0.104.tgz, r-oldrel (arm64): DeepLearningCausal_0.0.104.tgz, r-release (x86_64): DeepLearningCausal_0.0.104.tgz, r-oldrel (x86_64): DeepLearningCausal_0.0.104.tgz
Old sources: DeepLearningCausal archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.