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/>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
2026-04-09 windows/windows R-4.5 | CausalModels_0.2.1.zip |
104.8 KiB |