swMATH ID: 40350
Software Authors: Liangyuan Hu, Chenyang Gu, Michael Lopez, Jiayi Ji
Description: R package CIMTx: 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 Hu et al. <doi:10.1177/0962280220921909>.
Homepage: https://cran.r-project.org/web/packages/CIMTx/index.html
Source Code:  https://github.com/cran/CIMTx
Dependencies: R
Keywords: arXiv_stat.ME; arXiv_stat.AP; arXiv_stat.CO; R; R package; Comparative effectiveness research; Bayesian machine learning; sensitivity analysis; unmeasured confounding; common support
Related Software: BART; tmle; SuperLearner; drtmle; EValue; episensr; optmatch; Matching; MatchIt; sbw; optweight; PSW; causalweight; CBPS; ATE; WeightIt; twang; PSweight; R
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
CIMTx: An R package for causal inference with multiple treatments using observational data
Liangyuan Hu, Jiayi Ji