The Selectively Adaptive Lasso

@article{Schuler2022TheSA,
  title={The Selectively Adaptive Lasso},
  author={Alejandro Schuler and Mark J. van der Laan},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.10697}
}
Machine learning regression methods allow estimation of functions without unrealistic parametric assumptions. Although they can perform exceptionally in prediction error, most lack theoretical convergence rates necessary for semi-parametric efficient estimation (e.g. TMLE, AIPW) of parameters like average treatment effects. The Highly Adaptive Lasso (HAL) is the only regression method proven to converge quickly enough for a meaningfully large class of functions, independent of the dimensionality… 

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