Propensity Score Weighting for Causal Inference with Observational Studies and Randomized Trials

Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) and Li and Li (2019) .


Reference manual

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1.1.5 by Tianhui Zhou, a month ago

Browse source code at

Authors: Tianhui Zhou [aut, cre] , Guangyu Tong [aut] , Fan Li [aut] , Laine Thomas [aut] , Fan Li [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports nnet, MASS, ggplot2, numDeriv, gbm, SuperLearner

Suggests knitr, rmarkdown

Imported by causal.decomp.

See at CRAN