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Provide an accessible guide to targeted maximum likelihood estimation (TMLE) for estimating average treatment effects in observational epidemiology and assess its performance against standard methods.

November 1, 2016American Journal of Epidemiology

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies

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Provide an accessible guide to targeted maximum likelihood estimation (TMLE) for estimating average treatment effects in observational epidemiology and assess its performance against standard methods.
Presented step-by-step procedures for implementing TMLE to estimate average treatment effects in observational cohorts.
Compared TMLE conceptually and empirically against G-computation and inverse probability weighting.
Evaluated relative method performance via simulation studies under parametric model misspecification and integrated ensembled machine learning (super learning).
Simulations demonstrated the double robustness property of TMLE, maintaining validity when regression models were misspecified.
The incorporation of super learning across estimation procedures outperformed traditional parametric regression in observational settings.

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Implication

Simulation study demonstrates double robustness of targeted maximum likelihood estimation in observational data, highlighting the benefits of ensembled machine learning.

Cite This Study

Schuler et al. (2016) studied this question.

synapsesocial.com/papers/69d849828cb8f39931ae2d73 https://doi.org/10.1093/aje/kww165

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Implication

Simulation study demonstrates double robustness of targeted maximum likelihood estimation in observational data, highlighting the benefits of ensembled machine learning.

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies

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Discussion

Implication

Cite This Study

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies

Key Points

Discussion

Implication

Cite This Study

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