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Double/debiased machine learning for logistic partially linear model

Author

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  • Molei Liu
  • Yi Zhang
  • Doudou Zhou

Abstract

SummaryWe 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.

Suggested Citation

  • Molei Liu & Yi Zhang & Doudou Zhou, 2021. "Double/debiased machine learning for logistic partially linear model," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 559-588.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:3:p:559-588.
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    File URL: http://hdl.handle.net/10.1093/ectj/utab019
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    Cited by:

    1. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    2. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.

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