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The econometrics journal. 2021 Sep;24(3):559-588. doi: 10.1093/ectj/utab019 Q33.02024

Double/debiased machine learning for logistic partially linear model

逻辑部分线性模型的双重/无偏机器学习 翻译改进

Molei Liu  1, Y I Zhang  2, Doudou Zhou  3

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作者单位

  • 1 Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
  • 2 Department of Statistics, Harvard University, One Oxford Street, Cambridge, MA 02138-2901, USA.
  • 3 Department of Statistics, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
  • DOI: 10.1093/ectj/utab019 PMID: 38223304

    摘要 Ai翻译

    We propose double/debiased machine learning approaches to infer a parametric component of a logistic partially linear model. Our framework is based on a Neyman orthogonal score equation consisting of two nuisance models for the nonparametric component of the logistic model and conditional mean of the exposure with the control group. To estimate the nuisance models, we separately consider the use of high dimensional (HD) sparse regression and (nonparametric) machine learning (ML) methods. In the HD case, we derive certain moment equations to calibrate the first order bias of the nuisance models, which preserves the model double robustness property. In the ML case, we handle the nonlinearity of the logit link through a novel and easy-to-implement 'full model refitting' procedure. We evaluate our methods through simulation and apply them in assessing the effect of the emergency contraceptive pill on early gestation and new births based on a 2008 policy reform in Chile.

    Keywords: C14; Logistic partially linear model; calibration; double machine learning; double robustness; regularized regression.

    Keywords:machine learning

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    期刊名:Econometrics journal

    缩写:ECONOMET J

    ISSN:1368-4221

    e-ISSN:1368-423X

    IF/分区:3.0/Q3

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    Double/debiased machine learning for logistic partially linear model