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：我们考虑在存在干扰参数的情况下估计标量目标参数的问题。用非参数估计器（例如机器学习模型）替代未知的干扰参数虽简便易行，但已被证实因其较大的偏差而导致效率低下。现代方法，如目标最小损失估计（TMLE）和双重机器学习（DML），通过利用机器学习估计值同时减轻插值偏差，能在灵活假设下实现最优性能。为避免次优的偏差-方差权衡，这些方法会对初始插值估计执行去偏步骤。现有去偏方法需以目标参数的影响函数作为输入，然而推导影响函数需要专业知识，从而阻碍了从业者对这些方法的采用。我们提出了一种新型去偏插值估计方法，其优势在于：（i）高效性；（ii）无需实现影响函数；（iii）计算易处理，因此可快速适应新估计问题，无需用户进行解析推导即可实现自动化。我们基于TMLE框架，通过在由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值估计，从而为任意正则目标参数生成高效的插值估计。因此，我们的方法在提供去偏技术竞争性效率的同时，无需牺牲插值方法的实用性。