Targeted Learning

Causal Inference for Observational and Experimental Data

  • Mark J. van der Laan
  • Sherri Rose

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

Introduction

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

Keywords

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

  • Mark J. van der Laan
    • 1
  • Sherri Rose
    • 2
  1. 1., Division of BiostatisticsUniversity of California, BerkeleyBerkeleyUSA
  2. 2., Division of BiostatisticsUniversity of California, BerkeleyBerkeleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-9782-1
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-9781-4
  • Online ISBN 978-1-4419-9782-1
  • Series Print ISSN 0172-7397
  • About this book