We consider the problem of estimating a scalar target parameter in the presence of nuisance parameters. Replacing the unknown nuisance parameter with a nonparametric estimator, e.g.,a machine learning (ML) model, is convenient but has shown to be inefficient due to large biases. Modern methods, such as the targeted minimum loss-based estimation (TMLE) and double machine learning (DML), achieve optimal performance under flexible assumptions by harnessing ML estimates while mitigating the plug-in bias. To avoid a sub-optimal bias-variance trade-off, these methods perform a debiasing step of the plug-in pre-estimate. Existing debiasing methods require the influence function of the target parameter as input. However, deriving the IF requires specialized expertise and thus obstructs the adaptation of these methods by practitioners. We propose a novel way to debias plug-in estimators which (i) is efficient, (ii) does not require the IF to be implemented, (iii) is computationally tractable, and therefore can be readily adapted to new estimation problems and automated without analytic derivations by the user. We build on the TMLE framework and update a plug-in estimate with a regularized likelihood maximization step over a nonparametric model constructed with a reproducing kernel Hilbert space (RKHS), producing an efficient plug-in estimate for any regular target parameter. Our method, thus, offers the efficiency of competing debiasing techniques without sacrificing the utility of the plug-in approach.

翻译：我们研究在存在干扰参数的情况下标量目标参数的估计问题。用非参数估计器（例如机器学习模型）替换未知干扰参数虽方便，但因较大偏差而效率低下。现代方法如目标最小损失估计（TMLE）和双机器学习（DML）通过利用机器学习估计同时减轻插件偏差，在灵活假设下实现最优性能。为避免次优的偏差-方差权衡，这些方法对插件预估计执行去偏步骤。现有去偏方法需要目标参数的影响函数作为输入，但推导影响函数需要专门专业知识，阻碍了从业者对这些方法的适应。我们提出一种去偏插件估计器的新方法，该方法：（i）高效；（ii）无需实现影响函数；（iii）计算可处理，因此可轻松适应新估计问题并在无需用户解析推导的情况下实现自动化。我们基于TMLE框架，通过再生核希尔伯特空间构建的非参数模型上执行正则化似然最大化步骤来更新插件估计，为任何正则目标参数生成高效插件估计。因此，我们的方法在提供竞争性去偏技术效率的同时，不牺牲插件方法的实用性。