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) <https://miguelhernan.org/whatifbook/>).
| Version: |
0.2.1 |
| Imports: |
stats, causaldata, boot, multcomp, geepack |
| Published: |
2025-04-25 |
| DOI: |
10.32614/CRAN.package.CausalModels |
| Author: |
Joshua Anderson [aut, cre, cph],
Cyril Rakovski [rev],
Yesha Patel [rev],
Erin Lee [rev] |
| Maintainer: |
Joshua Anderson <jwanderson198 at gmail.com> |
| BugReports: |
https://github.com/ander428/CausalModels/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/ander428/CausalModels |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README, NEWS |
| CRAN checks: |
CausalModels results |
Documentation:
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