Year: 2022
Type: article
Abstract: [*CIMTx*](https://CRAN.R-project.org/package=CIMTx) provides efficient and unified functions to implement modern methods for causal inferences with multiple treatments using observational data with a focus on binary outcomes. The methods include regression adjustment, inverse probability of treatment weighting, Bayesian additive regression trees, regression adjustment with multivariate spline of the generalized propensity score, vector matching and targeted maximum likelihood estimation. In addition, [*CIMTx*](https://CRAN.R-project.org/package=CIMTx) illustrates ways in which users can simulate data adhering to the complex data structures in the multiple treatment setting. Furthermore, the [*CIMTx*](https://CRAN.R-project.org/package=CIMTx) package offers a unique set of features to address the key causal assumptions: positivity and ignorability. For the positivity assumption, [*CIMTx*](https://CRAN.R-project.org/package=CIMTx) demonstrates techniques to identify the common support region for retaining inferential units using inverse probability of treatment weighting, Bayesian additive regression trees and vector matching. To handle the ignorability assumption, [*CIMTx*](https://CRAN.R-project.org/package=CIMTx) provides a flexible Monte Carlo sensitivity analysis approach to evaluate how causal conclusions would be altered in response to different magnitude of departure from ignorable treatment assignment.
Source: The R Journal
DOI: 10.32614/rj-2022-058
Authors: Liangyuan Hu, Jiayi Ji
Institutions: Rutgers, The State University of New Jersey, Rutgers Health
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3206699728