Skip to main content
Unpublished Paper
Targeted Minimum Loss Based Estimator that Outperforms a given Estimator
U.C. Berkeley Division of Biostatistics Working Paper Series
  • Susan Gruber, University of California, Berkeley
  • Mark J. van der Laan, University of California - Berkeley
Date of this Version
4-8-2011
Abstract
Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally efficient double robust substitution estimators of the target parameter of the data generating distribution in a semiparametric censored data or causal inference model (van der Laan and Rubin (2006),van der Laan (2008), van der Laan and Rose (2011)). In this article we demonstrate how to construct a TMLE that also satisfies the property that it is at least as efficient as a user supplied asymptotically linear estimator. For the sake of illustration we focus on estimation of the additive average causal effect of a point treatment on an outcome, adjusting for baseline covariates.
Disciplines
Citation Information
Susan Gruber and Mark J. van der Laan. "Targeted Minimum Loss Based Estimator that Outperforms a given Estimator" (2011)
Available at: http://works.bepress.com/sgruber/16/