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Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Authors
  • Li, Haodong
  • Rosete, Sonali
  • Coyle, Jeremy
  • Phillips, Rachael V
  • Hejazi, Nima S
  • Malenica, Ivana
  • Arnold, Benjamin F
  • Benjamin-Chung, Jade
  • Mertens, Andrew
  • Colford, John M
  • van der Laan, Mark J
  • Hubbard, Alan E
Publication Date
May 01, 2022
Source
eScholarship - University of California
Keywords
License
Unknown
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Abstract

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.

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