Causal machine learning methods and use of sample splitting in settings with high-dimensional confounding

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, induced when there are many confounders relative to sample size, or complex relationships between continuous confounders and exposure and outcome. As a promising avenue to overcome this challenge, doubly robust methods (Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE)) enable the use of data-adaptive approaches to fit the two models they involve. Biased standard errors may result when the data-adaptive approaches used are very complex. The coupling of doubly robust methods with cross-fitting has been proposed to tackle this. Despite advances, limited evaluation, comparison, and guidance are available on the implementation of AIPW and TMLE with data-adaptive approaches and cross-fitting in realistic settings where high-dimensional confounding is present. 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) and 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 (SL) ensemble learning approach used for the data-adaptive models. A range of scenarios in terms of data generation, and sample size were considered. 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, with the number of folds a less important consideration. Using a full SL library was important to reduce bias and variance in the complex scenarios typical of modern health research studies.