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.
- Version1.2.0
- R versionunknown
- LicenseMIT
- Needs compilation?No
- Last release06/24/2022
Team
Jiayi Ji
Liangyuan Hu
Show author detailsRolesAuthorChenyang Gu
Show author detailsRolesAuthorMichael Lopez
Show author detailsRolesAuthor
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- Imports18 packages