txshift: Efficient estimation of the causal effects of stochastic interventions in R

R Submitted 20 May 2020Published 07 October 2020
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Editor: @marcosvital (all papers)
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Authors

Nima S. Hejazi (0000-0002-7127-2789), David Benkeser (0000-0002-1019-8343)

Citation

Hejazi et al., (2020). txshift: Efficient estimation of the causal effects of stochastic interventions in R. Journal of Open Source Software, 5(54), 2447, https://doi.org/10.21105/joss.02447

@article{Hejazi2020, doi = {10.21105/joss.02447}, url = {https://doi.org/10.21105/joss.02447}, year = {2020}, publisher = {The Open Journal}, volume = {5}, number = {54}, pages = {2447}, author = {Nima S. Hejazi and David Benkeser}, title = {`txshift`: Efficient estimation of the causal effects of stochastic interventions in `R`}, journal = {Journal of Open Source Software} }
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causal inference machine learning two-phase sampling efficient estimation targeted learning stochastic intervention modified treatment policy

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ISSN 2475-9066