bcf: Causal Inference using Bayesian Causal Forests

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <doi:10.48550/arXiv.1309.1906>.

Version: 2.0.2
Imports: Rcpp, RcppParallel, coda (≥ 0.19.3), Hmisc, parallel, doParallel, foreach, matrixStats
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: testthat, spelling, knitr, rmarkdown, latex2exp, ggplot2, rpart, rpart.plot, partykit
Published: 2024-02-27
Author: Jared S. Murray [aut, cre], P. Richard Hahn [aut], Carlos Carvalho [aut], Peter Mariani [ctb], Constance Delannoy [ctb], Mariel Finucane [ctb], Lauren V. Forrow [ctb], Drew Herren [ctb]
Maintainer: Jared S. Murray <jared.murray at mccombs.utexas.edu>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GNU make
Language: en-US
Citation: bcf citation info
Materials: README NEWS
CRAN checks: bcf results


Reference manual: bcf.pdf
Vignettes: A Simple Example
Prediction using BCF


Package source: bcf_2.0.2.tar.gz
Windows binaries: r-devel: bcf_2.0.2.zip, r-release: bcf_2.0.2.zip, r-oldrel: bcf_2.0.2.zip
macOS binaries: r-release (arm64): bcf_2.0.2.tgz, r-oldrel (arm64): bcf_2.0.2.tgz, r-release (x86_64): bcf_2.0.2.tgz
Old sources: bcf archive


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