Efficient estimation of pathwise differentiable target parameters with the undersmoothed highly adaptive lasso

J. van der Laan Mark (), Benkeser David and Cai Weixin
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J. van der Laan Mark: Division of Biostatistics, University of California, Berkeley, USA
Benkeser David: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
Cai Weixin: Division of Biostatistics, University of California, Berkeley, USA

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 261-289

Abstract: We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. The highly adaptive lasso estimator of the functional parameter is defined as the minimizer of the empirical risk over a class of cadlag functions with finite sectional variation norm, where the functional parameter is parametrized in terms of such a class of functions. In this article we establish that this HAL estimator yields an asymptotically efficient estimator of any smooth feature of the functional parameter under a global undersmoothing condition. It is formally shown that the L 1-restriction in HAL does not obstruct it from solving the score equations along paths that do not enforce this condition. Therefore, from an asymptotic point of view, the only reason for undersmoothing is that the true target function might not be complex so that the HAL-fit leaves out key basis functions that are needed to span the desired efficient influence curve of the smooth target parameter. Nonetheless, in practice undersmoothing appears to be beneficial and a simple targeted method is proposed and practically verified to perform well. We demonstrate our general result HAL-estimator of a treatment-specific mean and of the integrated square density. We also present simulations for these two examples confirming the theory.

Keywords: asymptotically efficient estimator; canonical gradient; cross-validation; highly adaptive lasso; sectional variation norm; undersmoothing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2019-0092

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