Web: http://arxiv.org/abs/2205.10697

Sept. 19, 2022, 1:13 a.m. | Alejandro Schuler, Mark van der Laan

stat.ML updates on arXiv.org arxiv.org

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 of the predictors.
Unfortunately, HAL is not computationally scalable. In this paper we build upon …

arxiv lasso

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