The Scalable Highly Adaptive Lasso

Authors: Jeremy Coyle, Nima Hejazi, and Mark van der Laan


What’s hal9001?

hal9001 is an R package providing an implementation of the scalable highly adaptive lasso (HAL), a nonparametric regression estimator that applies L1-regularized regression (i.e., the lasso) to a design matrix composed of indicator functions corresponding to a set of covariates and interactions thereof. Recent theoretical results show that HAL is endowed with several important optimality properties, making it well-suited for the estimation of highly complex functional forms while attaining fast convergence rates ((n^(1/4)) and better) when used in the estimation of nuisance functions. HAL has been quite successfully used in the construction of estimators at the intersection of semiparametric theory and nonparametric causal inference (e.g., the construction of efficient one-step or targeted minimum loss estimators).

For detailed discussions of the highly adaptive lasso estimator, consider consulting Benkeser and van der Laan (2016), van der Laan (2017a), and van der Laan (2017b), among other recent works.


Installation

To contribute, install the development version of hal9001 from GitHub via remotes:

remotes::install_github("tlverse/hal9001", build_vignettes = FALSE)

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.


Contributions

Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.


Citation

After using the hal9001 R package, please cite the following:

    @manual{coyle2019hal9001,
      author = {Coyle, Jeremy R and Hejazi, Nima S and {van der Laan}, Mark
        J},
      title = {{hal9001}: The scalable highly adaptive lasso},
      year  = {2019},
      howpublished = {\url{https://github.com/tlverse/hal9001}},
      note = {{R} package version 0.2.5},
      url = {https://doi.org/10.5281/zenodo.3558314},
      doi = {10.5281/zenodo.3558314}
    }

License

© 2017-2020 Jeremy R. Coyle & Nima S. Hejazi

The contents of this repository are distributed under the GPL-3 license. See file LICENSE for details.


References

Benkeser, David, and Mark J van der Laan. 2016. “The Highly Adaptive Lasso Estimator.” In *2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)*. IEEE. .
van der Laan, Mark J. 2017a. “A Generally Efficient Targeted Minimum Loss Based Estimator Based on the Highly Adaptive Lasso.” *The International Journal of Biostatistics*. De Gruyter. .
———. 2017b. “Finite Sample Inference for Targeted Learning.” *ArXiv E-Prints*.