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

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

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ISSN 2475-9066