Package: CausalModels 0.2.0

Joshua Anderson

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]

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CausalModels.pdf |CausalModels.html✨
CausalModels/json (API)
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Peer review:

Bug tracker:https://github.com/ander428/causalmodels/issues

On CRAN:

9 exports 12 stars 1.59 score 36 dependencies 1 scripts 263 downloads

Last updated 2 years agofrom:9c9ee22b66. Checks:OK: 7. Indexed: yes.

Exports:doubly_robustgestimationinit_paramsipweightingiv_estoutcome_regressionpropensity_matchingpropensity_scoresstandardization

Dependencies:backportsbootbroomcausaldataclicodetoolscpp11dplyrfansigeepackgenericsgluelatticelifecyclemagrittrMASSMatrixmultcompmvtnormpillarpkgconfigpurrrR6rlangsandwichstringistringrsurvivalTH.datatibbletidyrtidyselectutf8vctrswithrzoo