Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

by Mark J. van der Laan, Sherri Rose

Hardcover(1st ed. 2018)

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Product Details

ISBN-13: 9783319653037
Publisher: Springer International Publishing
Publication date: 03/29/2018
Series: Springer Series in Statistics
Edition description: 1st ed. 2018
Pages: 640
Sales rank: 1,235,007
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Mark van der Laan, PhD,is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics atUC Berkeley. His research interests include statistical methods in genomics, survival analysis,censored data, machine learning, semiparametric models, causal inference, and targetedlearning. His applied research involves applications in HIV and safety analysis, among others. He has published over 250 journal articles, 4 books, and one handbook on big data. Dr. van der Laan is also co-founder and co-editor of the International Journal of Biostatisticsand the Journal of Causal Inference and associate editor of a variety of journals. Dr. van derLaan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics or statistics.

Sherri Rose, PhD,is Associate Professor of Health Care Policy (Biostatistics) at Harvard MedicalSchool. Her work is centered on developing and integrating innovative statisticalapproaches to advance human health. Dr. Rose’smethodological research focuses on nonparametric machine learning for causal inference and prediction. She has made major contributions to the development and application oftargeted learning estimators, as well as adaptations to super learning for varied scientific problems. Within health policy, Dr. Rose works on comparative effectiveness research, health program impact evaluation, and computational health economics. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the AmericanStatistical Association and Biostatistics.

Table of Contents

Part I: Introductory Chapters

1. The Statistical Estimation Problem in Complex Longitudinal Data

  • Data Science and Statistical Estimation
  • Roadmap for Causal Effect Estimation
  • Role of Targeted Learning in Data Science
  • Observed Data
  • Caussal Model and Causal target Quantity
  • Statistical Model
  • Statistical Target Parameter
  • Statistical Estimation Problem

2. Longitudinal Causal Models

  • Structural Causal Models
  • Causal Graphs / DAGs
  • Nonparametric Structural Equation Models

3. Super Learner for Longitudinal Problems

  • Ensemble Learning
  • Sequential Regression

4. Longitudinal Targeted Maximum Likelihood Estimation (LTMLE)

  • Step-by-Step Demonstration of LTMLE
scalable inference="" for="" big="" data

5. Understanding LTMLE

  • Statistical Properties
  • Theoretical Background

6. Why LTMLE?

  • Landscape of Other Estimators
  • Comparison of Statistical Properties

Part II: Additional Core Topics

7. One-Step TMLE

  • General Framework
  • Theoretical Results

8. One-Step TMLE for the Effect Among the Treated

  • Demonstration for Effect Among the Treated
  • Simulation Studies

9. Online Targeted Learning

  • Batched Streaming Data
  • Online and One-Step Estimator
  • Theoretical Considerations

10. Networks

  • General Statistical Framework
  • Causal Model for Network Da
  • Counterfactual Mean Under Stochastic Intervention on the Network
  • Development of TMLE for Networks
  • Inference
  • 11. Application to Networks

    • Differing Network Structures
    • Realistic Network Examples (e.g., effect of vaccination)
    • R Package Implementation of TMLE

    12. Targeted Estimation of the Nuisance Parameter

    • Asymptotic Linearity
    • IPW
    • TMLE

    13. Sensitivity Analyses

    • General Nonparametric Approach to Sensitivity Analysis
    • Measurement Error
    • Unmeasured Confounding
    • Informative Missingness of the Outcome
    • FDA Meta-Analysis

    Part III: Randomized Trials

    14. Community Randomized Trials for Small Samples

    • Introduction of SEARCH Community Rando
    mized Trial
  • Adaptive Pair Matching
  • Data-Adaptive Selection of Covariates for Small Samples
  • TMLE Using Super Learning for Small Samples
  • Inference
  • 15. Sample Average Treatment Effect in a CRT

    • Introduction of the Parameter
    • Effect for the Observed Communities
    • Inference

    16. Application to Clinical Trial Survival Data

    • Introduction of the Survival Parameter
    • Censoring
    • Treatment-Specific Survival Function

    17. Application to Pandora Music Data

    • Effect of Pandora Streaming on Music Sales
    • Application of TMLE

    18. Causal Effect Transported Across Sites

    • Intent-to-Treat ATE
    • Complier ATE
    • Incomplete Data
    • Moving to Opportunity Trial

    Part IV: Observational Longitudinal Data

    19. Super Learning in the ICU

    • ICU Prediction Problem
    • Super Learning Algorithm

    • Defining Stochastic Interventions
    • Dependence on True Treatment Mechanisms
    • Continuous Exposure
    • Air Pollution Data Example

    21. Stochastic Multiple-Time-Point Interventions on Monitoring and Treatment

    • Defining Stochastic Interventions for Multiple-Time Points
    • Introduction of Monitoring Problem
    • Non-direct Effect Assumption of Monitoring
    • Dynamic Treatment
    • Diabetes Data Example

    22. Collaborative LTMLE

    • Collaborative LTMLE Framework
    • Breastfeeding Data Example

    Part V: Optimal Dynamic Regimes

    23. Targeted Adaptive Designs Learning the Optimal Dynamic Treatment

    • Group-Sequential Adaptive Designs
    • Multiple Bandit Problem
    • Treatment Allocation Learning from Past Data
    • Mean Outcome Under the Optimal Treatment
    • Martingale Theory
    • Inference

    24. Targeted Learning of the Optimal Dynamic Treatment

    • Super Learning for Discovering the Optimal Dynamic rule
    • Different Loss Functions
    • TMLE for the Counterfactual Mean
    • Statistical Inference for the Mean Outcome Under the Optimal Rule

    25. Optimal Dynamic Treatments Under Resource Constraints

    • Constrained Optimal Dynamic Treatment
    • Super Learning of the Constrained Optimal Dynamic Regime
    • TMLE of the Counterfactual Mean Under the Constrained
    Optimal Dynamic Regime

    Part VI: Computing

    26. ltmle() for R

    • Introduction to the ltmle() R Package
    • Demonstration of the ltmle() R Package

    27. Scaled Super Learner for R

    Introduction to the H2O Environment

    • R Package
    • Subsemble

    28. Scaling CTMLE for Julia

    • Scaling Computing of CTMLE in Julia
    • Pharmacoepidemiology Example

    Part VII: Special Topics

    29. Data-Adaptive Target Parameters

    • Definition of Parameter
    • Examples of Data-Adaptive Target Parameters as Arise in Data Mining
    • Estimators of the Data-Adaptive Target Parameters Using Sample Splitting
    • Estimators of the Data-Adaptive Target Parameters Without Sample Splitting
    • Cross-Validated TMLE of the Data-Adap
    tive Target Parameters

    30. Double Robust Inference for LTMLE

    • The Challenge of Double Robust Inference for Double Robust Estimators
    • 31. Higher-Order TMLE

      • Higher-Order Pathwise Differentiable Target Parameters
      • Higher-Order TMLE
      • Kth Order Remainder
      • Parameters Not Second-Order Pathwise Differentiable
      • Second-Order U Statistics
      • Approximate Second-Order Influence Function
      • Approximate Second-Order TMLE


      A. Online Targeted Learning Theory

      B. Computerization of the Calculation of Efficient Influence Curve

    C. TMLE Applied to Capture/Recapture

    D. TMLE for High Dimensional Linear Regression

    E. TMLE of Causal Effect Based on Observing a Single Time Series

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