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 as well as to attain fast convergence rates of nuisance functions via data-adaptive techniques (i.e., machine learning) in the context of nonparametric causal inference (e.g., the construction of targeted minimum loss-based estimators).

For detailed discussions of the Highly Adaptive Lasso estimator, the interested reader might consider consulting Benkeser and van der Laan (2016), van der Laan (2017a), and van der Laan (2017b).


You can install the development version of hal9001 from GitHub via devtools with

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


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


This minimal example shows how to use hal9001 to obtain predictions based on the Highly Adaptive Lasso. For details on the properties of the estimator, the interested reader is referred to Benkeser and van der Laan (2016) and van der Laan (2017a).


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


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

      author = {Coyle, Jeremy R and Hejazi, Nima S},
      title = {{hal9001}: The Scalable {Highly Adaptive Lasso}},
      year  = {2018},
      howpublished = {\url{}},
      url = {},
      doi = {}


© 2017-2019 Jeremy R. Coyle & Nima S. Hejazi

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


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.