Weighting for Covariate Balance in Observational Studies
Fitting Weighted Generalized Linear Models
User-Defined Functions for Estimating Weights
Estimate Balancing Weights
Generate Balancing Weights for Longitudinal Treatments
Create a weightit object manually
Calibrate Propensity Score Weights
Weighting methods
Compute effective sample size of weighted sample
Compute weights from propensity scores
Methods for glm_weightit() objects
Make a design matrix full rank
Propensity Score Weighting Using BART
Covariate Balancing Propensity Score Weighting
Entropy Balancing
Energy Balancing
Propensity Score Weighting Using Generalized Boosted Models
Propensity Score Weighting Using Generalized Linear Models
Inverse Probability Tilting
Nonparametric Covariate Balancing Propensity Score Weighting
Optimization-Based Weighting
Propensity Score Weighting Using SuperLearner
Plot information about the weight estimation process
Predictions for glm_weightit objects
Subgroup Balancing Propensity Score
Print and Summarize Output
Trim (Winsorize) Large Weights
WeightIt: Weighting for Covariate Balance in Observational Studies
Generate Balancing Weights with Minimal Input Processing
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.
Useful links