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

Lianyuan Hu, Jiayi Ji

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)213-230
Number of pages18
JournalR Journal
Volume14
Issue number3
DOIs
StatePublished - 2022
Externally publishedYes

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