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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="" data5. 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