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 (≥ 0.3.3.3.0) |
| 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 
[aut, cre] |
| Maintainer: |
Eric Dunipace <edunipace at mail.harvard.edu> |
| License: |
GPL (≥ 3.0) |
| NeedsCompilation: |
yes |
| Citation: |
causalOT citation info |
| Materials: |
README |
| CRAN checks: |
causalOT results |
Documentation:
Downloads:
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