TY - JOUR
T1 - CIMTx
T2 - An R Package for Causal Inference with Multiple Treatments using Observational Data
AU - Hu, Lianyuan
AU - Ji, Jiayi
N1 - Publisher Copyright:
© 2022, R Journal.All Rights Reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85143531480&partnerID=8YFLogxK
U2 - 10.32614/RJ-2022-058
DO - 10.32614/RJ-2022-058
M3 - Article
AN - SCOPUS:85143531480
SN - 2073-4859
VL - 14
SP - 213
EP - 230
JO - R Journal
JF - R Journal
IS - 3
ER -