Corpus ID: 219177037

A constrained sparse additive model for treatment effect-modifier selection

@article{Park2020ACS,
  title={A constrained sparse additive model for treatment effect-modifier selection},
  author={Hyung Park and Eva Petkova and Thaddeus Tarpey and Robert Todd Ogden},
  journal={arXiv: Methodology},
  year={2020}
}
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This paper develops a sparse additive model focused on estimation of treatment effect-modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified… Expand

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