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causalsim  

Simulation-Ready Causal Data Generating Processes
View on CRAN: Click here


Download and install causalsim package within the R console
Install from CRAN:
install.packages("causalsim")

Install from Github:
library("remotes")
install_github("cran/causalsim")

Install by package version:
library("remotes")
install_version("causalsim", "0.1.0")



Attach the package and use:
library("causalsim")
Maintained by
Chayce Reed
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-30
Latest Update: 2026-08-30
Description:
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.
How to cite:
Chayce Reed (2026). causalsim: Simulation-Ready Causal Data Generating Processes. R package version 0.1.0, https://cran.r-project.org/web/packages/causalsim. Accessed 04 Oct. 2026.
Previous versions and publish date:
No previous versions
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Complete documentation for causalsim
Functions, R codes and Examples using the causalsim R package
Full causalsim package functions and examples
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