Causal Inference for Multiple Treatments with a Binary Outcome

Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Liangyuan Hu (2020) and Jennifer L. Hill (2011) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("CIMTx")

0.1.0 by Jiayi Ji, a month ago


Browse source code at https://github.com/cran/CIMTx


Authors: Liangyuan Hu [aut] , Chenyang Gu [aut] , Michael Lopez [aut] , Jiayi Ji [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports nnet, BART, twang, arm, dplyr, Matching, magrittr, car, WeightIt, SuperLearner, tmle, tidyr, stats, class, gam


See at CRAN