medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R

R Submitted 10 November 2021Published 05 January 2022
Review

Editor: @mikldk (all papers)
Reviewers: @erikcs (all reviews), @rrrlw (all reviews)

Authors

Nima S. Hejazi (0000-0002-7127-2789), Kara E. Rudolph (0000-0002-9417-7960), Iván Díaz (0000-0001-9056-2047)

Citation

Hejazi et al., (2022). medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R. Journal of Open Source Software, 7(69), 3979, https://doi.org/10.21105/joss.03979

@article{Hejazi2022, doi = {10.21105/joss.03979}, url = {https://doi.org/10.21105/joss.03979}, year = {2022}, publisher = {The Open Journal}, volume = {7}, number = {69}, pages = {3979}, author = {Nima S. Hejazi and Kara E. Rudolph and Iván Díaz}, title = {`medoutcon`: Nonparametric efficient causal mediation analysis with machine learning in `R`}, journal = {Journal of Open Source Software} }
Copy citation string · Copy BibTeX  
Tags

causal inference machine learning semiparametric estimation mediation analysis natural direct effect interventional direct effect

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

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