Causal Learning of Mixed Graphical Models
Andrew J Sedgewick, Neha Abraham <neha.abraham@pitt.edu>, Vineet Raghu <vineetraghu@gmail.com>, Panagiotis Benos <benos@pitt.edu>
Allows users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables). To learn a directed graph over mixed data, it first calculates the undirected graph (Sedgewick et al, 2016) and then it uses local search strategies to prune-and-orient this graph (Sedgewick et al, 2017). AJ Sedgewick, I Shi, RM Donovan, PV Benos (2016) <doi:10.1186/s12859-016-1039-0>. AJ Sedgewick, JD Ramsey, P Spirtes, C Glymour, PV Benos (2017) <arXiv:1704.02621>.

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.
approxOT, Matrix, matrixStats, methods, lbfgsb3c, loo, osqp,
pbapply, reticulate, R6 (>= 2.4.1), Rcpp (>= 1.0.3), RSpectra,
sandwich
CBPS, data.table (>= 1.12.8), rstan (>= 2.19.3), Rmosek,
testthat (>= 2.1.0), knitr, rmarkdown

Causal Effect for Population Attributable Fractions (PAF)
c(person(given = "Maurice",
family = "O'Connell",
role = c("aut", "cph", "cre"),
email = "maurice.oconnell2@gmail.com",
comment = c(ORCID = "0000-0001-9658-495X")),
person(given = "John",
family = "Ferguson",
role = c("aut", "cph")))
Calculates population attributable fraction causal effects.
The 'causalPAF' package contains a suite of functions for causal
analysis calculations of population attributable fractions (PAF) given
a causal diagram which apply both: Pathway-specific population
attributable fractions (PS-PAFs) O’Connell and Ferguson (2020)
<doi:10.1101/2020.10.15.20212845> and Sequential population
attributable fractions Ferguson, O’Connell, and O’Donnell (2020)
<doi:10.1186/s13690-020-00442-x>.
Results are presentable in both table and plot format.
checkmate, dagitty, dplyr, forestplot, ggdag, ggplot2, grid,
gridExtra, magrittr, MASS, reshape2, rlist, splines, stats,
utils
Maurice O'Connell [aut, cph, cre]
(<https://orcid.org/0000-0001-9658-495X>),
John Ferguson [aut, cph]