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September 22, 2025Open Access

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation

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Bayesian approaches enhance the targeted maximum likelihood estimation method for better uncertainty quantification.
Results demonstrated that the Bayesian network-targeted maximum likelihood estimation outperformed classical methods in smaller data sets.
The study emphasized the importance of causality in decision making across critical domains like healthcare and finance.
Investigations revealed the effects of data sizes and model misspecifications on the accuracy of causal effect estimates.

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Bayesian implementation enhances targeted maximum likelihood estimation for robust decision making, indicating improved uncertainty predictions.

Nannapaneni et al. (2025) studied this question.

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Bayesian implementation enhances targeted maximum likelihood estimation for robust decision making, indicating improved uncertainty predictions.