title: Targeted minimum loss based estimator that outperforms a given estimator.
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. In particular it is shown that  this type of TMLE can incorporate empirical efficiency maximization as in  Rubin and van der Laan (2008),  Tan (2008, 2010), Rotnitzky et al. (2012), and retain double robustness. 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.
authors.affiliation: Harvard University, USA.
authors.forename: Susan
authors.initials: S
authors.lastname: Gruber
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
mesh_terms: D056808:Biostatistics / Q000379:methods*; D015984:Causality; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D016896:Treatment Outcome