CausalModels: Causal Inference Modeling for Estimation of Causal Effects

Provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <>).

Version: 0.1.0
Imports: stats, causaldata, boot, multcomp
Published: 2022-05-30
Author: Joshua Anderson [aut, cre, cph], Cyril Rakovski [rev], Yesha Patel [rev], Erin Lee [rev]
Maintainer: Joshua Anderson <jwanderson198 at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: CausalModels results


Reference manual: CausalModels.pdf


Package source: CausalModels_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): CausalModels_0.1.0.tgz, r-oldrel (arm64): CausalModels_0.1.0.tgz, r-release (x86_64): CausalModels_0.1.0.tgz, r-oldrel (x86_64): CausalModels_0.1.0.tgz


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