Targeted Maximum Likelihood Estimation: A Gentle Introduction

This paper provides a concise introduction to targeted maximum likelihood estimation (TMLE) of causal effect parameters. The interested analyst should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. A program written in R is provided. This program implements a basic version of TMLE that can be used to estimate the effect of a binary point treatment on a continuous or binary outcome. Targeted Maximum Likelihood Estimation: A Gentle Introduction Susan Gruber and Mark J. van der Laan Division of Biostatistics, University of California, Berkeley sgruber@berkeley.edu, laan@berkeley.edu

[1]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[2]  James M. Robins,et al.  On Profile Likelihood: Comment , 2000 .

[3]  J. Robins,et al.  Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. , 2000, Epidemiology.

[4]  M. J. van der Laan,et al.  The International Journal of Biostatistics Collaborative Double Robust Targeted Maximum Likelihood Estimation , 2011 .

[5]  M J van der Laan,et al.  Covariate adjustment in randomized trials with binary outcomes: Targeted maximum likelihood estimation , 2009, Statistics in medicine.

[6]  Mark J van der Laan,et al.  Super Learning: An Application to the Prediction of HIV-1 Drug Resistance , 2007, Statistical applications in genetics and molecular biology.

[7]  Peter J. Bickel,et al.  INFERENCE FOR SEMIPARAMETRIC MODELS: SOME QUESTIONS AND AN ANSWER , 2001 .

[8]  Stanton East,et al.  Linear Statistical Models for Causation: A Critical Review , 2005 .

[9]  M. J. van der Laan,et al.  The International Journal of Biostatistics Targeted Maximum Likelihood Learning , 2011 .

[10]  C. Klaassen,et al.  Discussion to "Inference for semiparametric models: some questions and an answer" by Peter J. Bickel and Jaimyoung Kwon , 2001 .

[11]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[12]  Annette M. Molinaro,et al.  partDSA: deletion/substitution/addition algorithm for partitioning the covariate space in prediction , 2010, Bioinform..

[13]  James M. Robins,et al.  Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .