© 2011

Targeted Learning

Causal Inference for Observational and Experimental Data


Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-lxxi
  2. Targeted Learning: The Basics

    1. Front Matter
      Pages 1-1
    2. Sherri Rose, Mark J. van der Laan
      Pages 3-20
    3. Sherri Rose, Mark J. van der Laan
      Pages 21-42
    4. Eric C. Polley, Sherri Rose, Mark J. van der Laan
      Pages 43-66
    5. Sherri Rose, Mark J. van der Laan
      Pages 67-82
    6. Sherri Rose, Mark J. van der Laan
      Pages 83-100
    7. Sherri Rose, Mark J. van der Laan
      Pages 101-118
  3. Additional Core Topics

    1. Front Matter
      Pages 119-119
    2. Susan Gruber, Mark J. van der Laan
      Pages 121-132
    3. Alan E. Hubbard, Nicholas P. Jewell, Mark J. van der Laan
      Pages 133-143
    4. Michael Rosenblum
      Pages 145-160
    5. Maya L. Petersen, Kristin E. Porter, Susan Gruber, Yue Wang, Mark J. van der Laan
      Pages 161-184
  4. TMLE and Parametric Regression in RCTs

    1. Front Matter
      Pages 185-185
    2. Daniel B. Rubin, Mark J. van der Laan
      Pages 201-215
  5. Case-Control Studies

    1. Front Matter
      Pages 217-217
    2. Sherri Rose, Mark J. van der Laan
      Pages 219-228
    3. Sherri Rose, Mark J. van der Laan
      Pages 229-238
    4. Sherri Rose, Bruce Fireman, Mark J. van der Laan
      Pages 239-245

About this book


The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest.
This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

"Targeted Learning, by Mark J. van der Laan and Sherri Rose, fills a much needed gap in statistical and causal inference. It protects us from wasting computational, analytical, and data resources on irrelevant aspects of a problem and teaches us how to focus on what is relevant – answering questions that researchers truly care about."
-Judea Pearl, Computer Science Department, University of California, Los Angeles

"In summary, this book should be on the shelf of every investigator who conducts observational research and randomized controlled trials. The concepts and methodology are foundational for causal inference and at the same time stay true to what the data at hand can say about the questions that motivate their collection."
-Ira B. Tager, Division of Epidemiology, University of California, Berkeley


Causal inference High-dimensional and complex data Nonparametric and semiparametric statistics Observational studies Prediction Randomized controlled trials Super (machine) learning Targeted maximum likelihood estimation Time-dependent confounding

Authors and affiliations

  1. 1., Division of BiostatisticsUniversity of California, BerkeleyBerkeleyUSA
  2. 2., Division of BiostatisticsUniversity of California, BerkeleyBerkeleyUSA

About the authors

Mark J. van der Laan is a Hsu/Peace Professor of Biostatistics and Statistics at the University of California, Berkeley.  His research concerns causal inference, prediction, adjusting for missing and censored data, and estimation based on high-dimensional observational and experimental biomedical and genomic data.  He is the recipient of the 2005 COPSS Presidents’ and Snedecor Awards, as well as the 2004 Spiegelman Award, and is a Founding Editor for the International Journal of Biostatistics.

Sherri Rose is currently a PhD candidate in the Division of Biostatistics at the University of California, Berkeley.  Her research interests include causal inference, prediction, and applications in rare diseases. Upon completion of her doctoral degree, she will begin an NSF Mathematical Sciences Postdoctoral Research Fellowship at Johns Hopkins Bloomberg School of Public Health.

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From the reviews:

“This book is a timely fit and is expected to draw much attention from researchers in the field of causal inference. The book explains the concept of targeted learning, which is an enhanced procedure for estimating targeted causal estimands under the potential outcome framework. … Excellent summaries of complex estimation procedures and methods are ubiquitous, which will be helpful for the nontechnical readers of the book. … This book appears to be a useful reference for Ph.D. students in biostatistics programs.” (Joseph Kang, Journal of the American Statistical Association, June, 2013)