Package: CausalModels 0.2.0

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/>).

Authors:Joshua Anderson [aut, cre, cph], Cyril Rakovski [rev], Yesha Patel [rev], Erin Lee [rev]