CausalModels: Causal Inference Modeling for Estimation of Causal Effects (original) (raw)
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 |
| 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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