Abstract
A TMLE of a causal quantity of interest first constructs an initial estimator of the relevant part of the likelihood of the data and then updates this initial estimator along a least favorable parametric model that uses the initial estimator as an off-set. The least favorable parametric model typically depends on an orthogonal nuisance parameter such as the treatment and censoring mechanism. This nuisance parameter is not needed to evaluate the target parameter, and, in fact, is orthogonal to the target parameter in the sense that a maximum likelihood estimator would completely ignore this nuisance parameter, or, at least, its scores are orthogonal to the scores of the relevant part of the likelihood.
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van der Laan, M.J., Chambaz, A., Ju, C. (2018). C-TMLE for Continuous Tuning. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_10
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DOI: https://doi.org/10.1007/978-3-319-65304-4_10
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