causalOT: Optimal Transport Weights for Causal Inference

Uses optimal transport distances to find probabilistic matching estimators for causal inference. These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>. The package will build the weights, estimate treatment effects, and calculate confidence intervals via the methods described in the paper. The package also supports several other methods as described in the help files.

Version: 0.1.2
Depends: R (≥ 3.5.0)
Imports: approxOT, Matrix, matrixStats, methods, lbfgsb3c, loo, osqp, pbapply, reticulate, R6 (≥ 2.4.1), Rcpp (≥ 1.0.3), RSpectra, sandwich
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥
Suggests: CBPS, data.table (≥ 1.12.8), rstan (≥ 2.19.3), Rmosek, testthat (≥ 2.1.0), knitr, rmarkdown
Published: 2022-09-04
Author: Eric Dunipace ORCID iD [aut, cre]
Maintainer: Eric Dunipace <edunipace at>
License: GPL (≥ 3.0)
NeedsCompilation: yes
Citation: causalOT citation info
Materials: README
CRAN checks: causalOT results


Reference manual: causalOT.pdf
Vignettes: Using causalOT


Package source: causalOT_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): causalOT_0.1.2.tgz, r-oldrel (arm64): causalOT_0.1.2.tgz, r-release (x86_64): causalOT_0.1.2.tgz, r-oldrel (x86_64): causalOT_0.1.2.tgz
Old sources: causalOT archive


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