Evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. A traditional approach uses the back-door criterion, seeking adjustment sets to block confounding paths between treatment and outcome. However, this method struggles with unmeasured confounders. As an alternative, the front-door criterion offers a solution, even in the presence of unmeasured confounders between treatment and outcome. This method relies on identifying mediators that are not directly affected by these confounders and that completely mediate the treatment's effect. Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory. Our estimators work across diverse scenarios, handling binary, continuous, and multivariate mediators. They leverage data-adaptive machine learning algorithms, minimizing assumptions and ensuring key statistical properties like asymptotic linearity, double-robustness, efficiency, and valid estimates within the target parameter space. We establish conditions under which the nuisance functional estimations ensure the root n-consistency of ACE estimators. Our numerical experiments show the favorable finite sample performance of the proposed estimators. We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income using data from the Finnish Social Science Data Archive.

翻译：在观察性研究中，评估处理变量对结果变量的平均因果效应常面临混杂因素带来的挑战。传统方法使用后门准则，通过寻找调整集阻断处理与结果之间的混杂路径，但该方法无法处理未测量的混杂变量。作为替代方案，前门准则即使在处理与结果之间存在未测量混杂时仍能提供解决方案，该方法依赖于识别不受混杂直接影响且完全中介处理效应的中介变量。本文基于目标最小损失估计理论，提出前门准则的新型估计策略。所提出的估计量适用于二元、连续及多元中介变量等多样化场景，利用数据自适应机器学习算法，在最小化假设的前提下确保关键统计性质，包括渐近线性性、双重稳健性、有效性及目标参数空间内的有效估计。我们建立了扰动函数估计保障平均因果效应估计量根号n一致性的条件。数值实验表明所提估计量在有限样本中表现优异。我们利用芬兰社会科学数据档案馆的数据，实证分析了早期学业表现对未来年收入的影响。