Summary 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.

中文翻译：

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逻辑部分线性模型的双/去偏机器学习

总结 我们提出了双重/去偏机器学习方法来推断逻辑部分线性模型的参数组件。我们的框架基于 Neyman 正交评分方程，该方程由逻辑模型的非参数分量的两个讨厌模型和对照组暴露的条件平均值组成。为了估计讨厌的模型，我们分别考虑使用高维（HD）稀疏回归和（非参数）机器学习（ML）方法。在 HD 情况下，我们推导出某些矩方程来校准滋扰模型的一阶偏差，从而保留了模型的双重鲁棒性。在 ML 案例中，我们通过一种新颖且易于实现的“全模型改装”程序来处理 logit 链接的非线性。