Main description:

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011.

Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics.

Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose's methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

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

ta
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

Appendices

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