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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://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>).
Getting started
Browse package contents
Package details |
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| Author | Joshua Anderson [aut, cre, cph], Cyril Rakovski [rev], Yesha Patel [rev], Erin Lee [rev] |
| Maintainer | Joshua Anderson <jwanderson198@gmail.com> |
| License | GPL-3 |
| Version | 0.2.0 |
| URL | https://github.com/ander428/CausalModels |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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