We give debiased machine learners of parameters of interest that depend on generalized linear regressions, which regressions make a residual orthogonal to regressors. The parameters of interest include many causal and policy effects. We give neural net learners of the bias correction that are automatic in only depending on the object of interest and the regression residual. Convergence rates are given for these neural nets and for more general learners of the bias correction. We also give conditions for asymptotic normality and consistent asymptotic variance estimation of the learner of the object of interest.

翻译：我们向受偏差的机器学习者提供受普遍线性回归影响的兴趣参数,这种回归使残余的正反向向向递减者产生一个残余的正反向反向反应者。这些利益参数包括许多因果关系和政策影响。我们向神经净学习者提供只根据兴趣对象和回归残余而自动进行的偏向修正。对这些神经网和偏向修正的更普通的学习者提供趋同率。我们还提供了对利息对象学习者进行无症状正常和一贯无症状差异估计的条件。