Towards robust causal inference in epidemiologic research: employing double cross-fit TMLE in right heart catheterization data.

ABSTRACT: Within epidemiologic 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 undercoverage. 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 epidemiologic research, primarily due to the lack of comprehensive methodologi

SUBMITTER: Mondol MH 

PROVIDER: S-EPMC12527275 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature