Towards Robust Causal Inference in Epidemiological Research: Employing Double Cross-fit TMLE in Right Heart Catheterization Data.

Abstract

Within epidemiological research, estimating treatment effects from observational data presents notable challenges. Targeted Maximum Likelihood Estimation (TMLE) emerges as a robust method, addressing these challenges by accurately modeling treatment effects. This approach uniquely combines the precision of correctly specified models with the versatility of data-adaptive, flexible machine learning algorithms. Despite its effectiveness, TMLE's integration of complex algorithms can introduce bias and under-coverage. This issue is addressed through the Double Cross-fit TMLE (DC-TMLE) approach, enhancing accuracy and reducing biases inherent in observational studies. However, DC-TMLE's potential remains underexplored in epidemiological research, primarily due to the lack of comprehensive methodological guidance and the complexity of its computational implementation. Recognizing this gap, our paper contributes a detailed, reproducible guide for implementing DC-TMLE in R, aimed specifically at epidemiological applications. We demonstrate the utility of this method using an openly available clinical dataset, underscoring its relevance and adaptability for robust epidemiological analysis. This guide aims to facilitate broader adoption of DC-TMLE in epidemiological studies, promoting more accurate and reliable treatment effect estimations in observational research.