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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-1-4419-9782-1_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9781-4
Online ISBN: 978-1-4419-9782-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)