CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R

R Submitted 04 February 2022 • Published 21 February 2023
Software repository Paper review Download paper Software archive
Review

Editor: @osorensen (all papers)
Reviewers: @GaryBAYLOR (all reviews), @cpalmer718 (all reviews), @wleoncio (all reviews)

Authors

David McCoy (0000-0002-5515-6307), Alan Hubbard (0000-0002-3769-0127), Mark Van der Laan (0000-0003-1432-5511)

Citation

McCoy et al., (2023). CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R. Journal of Open Source Software, 8(82), 4181, https://doi.org/10.21105/joss.04181

@article{McCoy2023, doi = {10.21105/joss.04181}, url = {https://doi.org/10.21105/joss.04181}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {82}, pages = {4181}, author = {David McCoy and Alan Hubbard and Mark Van der Laan}, title = {CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  
Tags

causal inference machine learning decision trees efficient estimation targeted learning iterative backfitting mixed exposures

Altmetrics
Markdown badge

 

License

Authors of JOSS papers retain copyright.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License

Journal of Open Source Software is an affiliate of the Open Source Inititative.

Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.

Table of Contents
Public user content licensed CC BY 4.0 unless otherwise specified.
ISSN 2475-9066