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 |
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