Abstract
This chapter focuses on understanding TMLE. We go into more detail than the previous chapter to demonstrate how this estimator is derived. Recall that TMLE is a two-step procedure where one first obtains an estimate of the data-generating distribution P0 or the relevant portion Q0 of P0. The second stage updates this initial fit in a step targeted toward making an optimal bias–variance tradeoff for the parameter of interest ψ(Q0), instead of the overall density P0. The procedure is double robust and can incorporate data-adaptive-likelihood-based estimation procedures to estimate Q0 and the treatment mechanism.
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© 2011 Springer Science+Business Media, LLC
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Rose, S., van der Laan, M.J. (2011). Understanding TMLE. In: Targeted Learning. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9782-1_5
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DOI: https://doi.org/10.1007/978-1-4419-9782-1_5
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9781-4
Online ISBN: 978-1-4419-9782-1
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