causalsim: Simulation-Ready Causal Data Generating Processes

Construct, simulate, and evaluate causal data generating processes (DGPs) with known ground truth. Designed for benchmarking causal estimators, studying confounding and treatment-effect heterogeneity, and building reproducible teaching examples. Covariate roles (confounder, effect modifier, noise) and heterogeneous treatment effects are first-class concepts in the API, and estimator performance is summarised with bias, root mean squared error, confidence-interval coverage, and power.

Version: 0.1.0
Depends: R (≥ 4.0.0)
Suggests: knitr, pkgdown, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-08-30
DOI: 10.32614/CRAN.package.causalsim (may not be active yet)
Author: Chayce Reed [aut, cre]
Maintainer: Chayce Reed <Chayce.Reed.HSE at dartmouth.edu>
BugReports: https://github.com/chaycereed/causalsim/issues
License: MIT + file LICENSE
URL: https://chaycereed.github.io/causalsim/, https://github.com/chaycereed/causalsim
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: causalsim results

Documentation:

Reference manual: causalsim.html , causalsim.pdf
Vignettes: A Complete Simulation Study with causalsim (source, R code)

Downloads:

Package source: causalsim_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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