🤖 AI Summary
Estimating causal effects in observational studies is challenging when unmeasured confounding violates the backdoor criterion. Method: This paper develops an average causal effect (ACE) estimation framework based on the front-door criterion, leveraging mediators to circumvent unobserved confounding paths. It systematically integrates targeted minimum loss estimation (TMLE) theory with front-door identification, accommodating binary, continuous, and multivariate mediators. The resulting estimator is √n-consistent, doubly robust, and semiparametrically efficient. The method combines data-adaptive machine learning (e.g., gradient boosting, random forests) with functional modeling of the front-door formula. Contribution/Results: Empirical evaluation on simulated data and real Finnish education–income data demonstrates that the proposed approach significantly improves finite-sample estimation accuracy and stability over existing methods, enabling reliable quantification of the causal impact of early academic performance on adult income.
📝 Abstract
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.
