Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, which occurs when there are many confounders relative to sample size or complex relationships between continuous confounders and exposure and outcome. Despite recent advances, limited evaluation, and guidance are available on the implementation of doubly robust methods, Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE), with data-adaptive approaches and cross-fitting in realistic settings where high-dimensional confounding is present. Motivated by an early-life cohort study, we conducted an extensive simulation study to compare the relative performance of AIPW and TMLE using data-adaptive approaches in estimating the average causal effect (ACE). We evaluated the benefits of using cross-fitting with a varying number of folds, as well as the impact of using a reduced versus full (larger, more diverse) library in the Super Learner ensemble learning approach used for implementation. We found that AIPW and TMLE performed similarly in most cases for estimating the ACE, but TMLE was more stable. Cross-fitting improved the performance of both methods, but was more important for estimation of standard error and coverage than for point estimates, with the number of folds a less important consideration. Using a full Super Learner library was important to reduce bias and variance in complex scenarios typical of modern health research studies.

翻译：观察性流行病学研究通常旨在估计暴露对结果的因果效应。现代研究中由于高维混杂的存在，调整潜在混杂偏倚具有挑战性——当混杂变量数量相对于样本量较多，或连续混杂变量与暴露及结果之间存在复杂关系时，便会出现高维混杂。尽管近期研究取得进展，但在实际存在高维混杂的场景中，关于如何将双重稳健方法（增强逆概率加权AIPW与靶向最大似然估计TMLE）与数据自适应方法及交叉拟合结合实施，目前仍缺乏充分的评估与指导。基于一项生命早期队列研究，我们开展了广泛的模拟研究，比较AIPW和TMLE在使用数据自适应方法估计平均因果效应（ACE）时的相对性能。我们评估了使用不同折数的交叉拟合的优势，以及在实施所用的Super Learner集成学习方法中，使用精简模型库与完整（更大、更多样化）模型库的影响。研究发现，在大多数情况下AIPW和TMLE在估计ACE时表现相似，但TMLE更为稳定。交叉拟合提升了两种方法的性能，但其对标准误估计和覆盖率的改善作用比对点估计更为重要，且折数选择的影响相对较小。在现代健康研究常见的复杂场景中，使用完整的Super Learner模型库对于降低偏倚和方差至关重要。