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Causal Machine Learning Methods and Use of Cross-Fitting in Settings With High-Dimensional Confounding.

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  • 1Murdoch Children's Research Institute, Parkville, Victoria, Australia.

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Summary
This summary is machine-generated.

Targeted Maximum Likelihood Estimation (TMLE) and Augmented Inverse Probability Weighting (AIPW) methods showed similar performance for estimating causal effects. TMLE offered greater stability, and cross-fitting improved variance estimation, especially in complex observational studies.

Keywords:
augmented inverse probability weightingcausal inferencecross‐fittingdoubly robusthigh‐dimensional confoundingtargeted maximum likelihood estimation

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Observational studies aim to estimate causal effects but face challenges from high-dimensional confounding.
  • Doubly robust methods like AIPW and TMLE offer potential solutions using data-adaptive techniques.

Purpose of the Study:

  • To compare the performance of AIPW and TMLE for estimating average causal effects (ACE) in the presence of high-dimensional confounding.
  • To evaluate the impact of cross-fitting and Super Learner library size on method performance.

Main Methods:

  • Extensive simulation study using an early-life cohort as motivation.
  • Comparison of Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE).
  • Evaluation of data-adaptive approaches, cross-fitting with varying folds, and Super Learner library variations.

Main Results:

  • AIPW and TMLE demonstrated similar point estimate performance for ACE.
  • TMLE exhibited superior stability compared to AIPW.
  • Cross-fitting enhanced variance estimation and coverage, more so than point estimates.
  • A full Super Learner library was crucial for reducing bias and variance in complex scenarios.

Conclusions:

  • Both AIPW and TMLE are viable doubly robust methods for high-dimensional confounding.
  • TMLE's stability and the benefits of cross-fitting and comprehensive Super Learner libraries are key for reliable causal effect estimation in modern epidemiological research.