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Evaluating the average causal effect (ACE) of a treatment on an outcome ofteninvolves overcoming the challenges posed by confounding factors inobservational studies. A traditional approach uses the back-door criterion,seeking adjustment sets to block confounding paths between treatment andoutcome. However, this method struggles with unmeasured confounders. As analternative, the front-door criterion offers a solution, even in the presenceof unmeasured confounders between treatment and outcome. This method relies onidentifying mediators that are not directly affected by these confounders andthat completely mediate the treatment's effect. Here, we introduce novelestimation strategies for the front-door criterion based on the targetedminimum loss-based estimation theory. Our estimators work across diversescenarios, handling binary, continuous, and multivariate mediators. Theyleverage data-adaptive machine learning algorithms, minimizing assumptions andensuring key statistical properties like asymptotic linearity,double-robustness, efficiency, and valid estimates within the target parameterspace. We establish conditions under which the nuisance functional estimationsensure the root n-consistency of ACE estimators. Our numerical experiments showthe favorable finite sample performance of the proposed estimators. Wedemonstrate the applicability of these estimators to analyze the effect ofearly stage academic performance on future yearly income using data from theFinnish Social Science Data Archive.

Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional