# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "DeepLearningCausal" in publications use:' type: software license: GPL-3.0-only title: 'DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks' version: 0.0.104 doi: 10.32614/CRAN.package.DeepLearningCausal abstract: 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) 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) PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks. authors: - family-names: Huynh given-names: Nguyen K. email: khoinguyen.huynh@r.hit-u.ac.jp orcid: https://orcid.org/0000-0002-6234-7232 - family-names: Mukherjee given-names: Bumba email: bumba.mukherjee@psu.edu orcid: https://orcid.org/0000-0002-3453-601X - family-names: Lee given-names: Irvin (Chen-Yu) email: cvl6079@psu.edu orcid: https://orcid.org/0009-0004-5913-8925 repository: https://CRAN.R-project.org/package=DeepLearningCausal repository-code: https://github.com/hknd23/DeepLearningCausal url: https://github.com/hknd23/DeepLearningCausal date-released: '2024-07-30' contact: - family-names: Huynh given-names: Nguyen K. email: khoinguyen.huynh@r.hit-u.ac.jp orcid: https://orcid.org/0000-0002-6234-7232