Description
Book SynopsisThis 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.
Trade Review“A list of abbreviations, including all the statistical terms used in the textbook, as well as a list of tables and figures would be a welcome addition to the book. This may be particularly useful as the TMLE is a very important application in parametric statistics, and may be used by biostatisticians … . Specifically, those with a very good knowledge of advanced theoretical statistics, including the observational and modeling statistics that are almost prerequisite for appreciating this textbook.” (Ramzi El Feghali, ISCB News, iscb.info, Issue 67, June, 2019)
“The book recommends itself as a thorough overview of TMLE approaches with a variety of examples and case studies, all presented in detail, in a text-book like manner, making this work accessible to a wide audience from undergraduates to established researchers.” (Irina Ioana Mohorianu, zbMATH 1408.62005, 2019)
Table of ContentsPart 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
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
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
