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

TL;DR

CIMTx is an R package that offers comprehensive tools for causal inference with multiple treatments in observational studies, focusing on binary outcomes and addressing key assumptions like positivity and ignorability.

Contribution

The package integrates modern causal inference methods and provides simulation and sensitivity analysis features tailored for complex multiple treatment observational data.

Findings

Includes multiple causal inference methods like BART, TMLE, and vector matching.

Provides tools for identifying common support regions in treatment groups.

Offers sensitivity analysis to assess violations of ignorability.

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 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 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.