PAGId R Package: Algorithms for (Conditional) Causal Effect Identification in Partial Ancestral Graphs

Overview

This package implements the CIDP and IDP algorithms for identifying (conditional) causal effects from a Partial Ancentral Graph (PAG). Technical details are provided in the NeurIPS 2022 paper by Jaber A., Ribeiro A. H., Zhang J., and Bareinboim E., (2022) entitled "Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness".

Requirements:

First, install R (>= 3.5.0) and the following necessary R packages:

install.packages(c("dagitty", "pcalg"), dependencies=TRUE)

If you also want to run the simulations, then the following R packages are also necessary:

install.packages(c("bnlearn", "causaleffect", "igraph"), dependencies=TRUE)

Installation:

We can now proceed with the installation of the PAGId R package.

You can download the latest tar.gz file with the source code of the PAGId R package, available at https://github.com/adele/PAGId/releases/latest, and install it with the following command, where path_to_file represents the full path and file name of the tar.gz file:

install.packages(path_to_file, repos=NULL, type="source", dependencies=TRUE)

Or you can install the development version directly from GitHub. Make sure you have the devtools R package installed. If not, install it with install.packages("devtools", dependencies=TRUE).

devtools::install_github("adele/PAGId", dependencies=TRUE)