Cross-Validated Targeted Minimum-Loss-Based Estimation
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In previous chapters, we introduced targeted maximum likelihood estimation in semiparametric models, which incorporates adaptive estimation (e.g., loss-based super learning) of the relevant part of the data-generating distribution and subsequently carries out a targeted bias reduction by maximizing the log-likelihood, or minimizing another loss-specific empirical risk, over a “clever” parametric working model through the initial estimator, treating the initial estimator as offset. This updating process may need to be iterated to convergence. The target parameter of the resulting updated estimator is then evaluated, and is called the targeted minimum-loss- based estimator (also TMLE) of the target parameter of the data-generating distribution. This estimator is, by definition, a substitution estimator, and, under regularity conditions, is a double robust semiparametric efficient estimator.
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