CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data.

ABSTRACT: 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 illustrates ways in which users can simulate data adhering to the complex data structures in the multiple treatment setting. Furthermore, the CIMTx package offers a unique set of features to address the key causal assumptions: positivity and ignorability. For the positivity assumption, CIMTx demonstrates

SUBMITTER: Hu L 

PROVIDER: S-EPMC11415261 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature