WeightIt: Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.

See also

Useful links:

• https://ngreifer.github.io/WeightIt/

• https://github.com/ngreifer/WeightIt

• Report bugs at https://github.com/ngreifer/WeightIt/issues

Author

Maintainer: Noah Greifer noah.greifer@gmail.com (ORCID)