CIMTx: An R package for causal inference with multiple treatments using observational data

CIMTx provides a streamlined approach to implement various methods designed to draw causal inferences about 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 identification assumptions: positivity and ignorability. For the positivity assumption, CIMTx demonstrates techniques to identify the common support region for retaining inferential units. The ignorability assumption can be violated in observational studies when there exists unmeasured confounding. CIMTx provides a flexible Monte Carlo sensitivity analysis approach to evaluate how causal conclusions would change in response to different magnitude of departure from the ignorability assumption.