目次

Part I:  Introductory Chapters 1.  The Statistical Estimation Problem in Complex Longitudinal Data Data Science and Statistical EstimationRoadmap for Causal Effect EstimationRole of Targeted Learning in Data ScienceObserved DataCaussal Model and Causal target QuantityStatistical ModelStatistical Target ParameterStatistical Estimation Problem 2.  Longitudinal Causal Models Structural Causal ModelsCausal Graphs / DAGsNonparametric Structural Equation Models 3.  Super Learner for Longitudinal Problems Ensemble LearningSequential Regression 4.  Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) Step-by-Step Demonstration of LTMLEscalable inference="" for="" big="" data 5.  Understanding LTMLE Statistical PropertiesTheoretical Background 6.  Why LTMLE? Landscape of Other EstimatorsComparison of Statistical Properties   Part II:  Additional Core Topics 7.  One-Step TMLE General FrameworkTheoretical Results 8.  One-Step TMLE for the Effect Among the Treated Demonstration for Effect Among the TreatedSimulation Studies 9.  Online Targeted Learning Batched Streaming DataOnline and One-Step EstimatorTheoretical Considerations 10.  Networks General Statistical FrameworkCausal Model for Network DataCounterfactual Mean Under Stochastic Intervention on the NetworkDevelopment of TMLE for NetworksInference 11. Application to Networks Differing Network StructuresRealistic Network Examples (e.g., effect of vaccination)R Package Implementation of TMLE 12. Targeted Estimation of the Nuisance Parameter Asymptotic LinearityIPWTMLE 13. Sensitivity AnalysesGeneral Nonparametric Approach to Sensitivity AnalysisMeasurement ErrorUnmeasured ConfoundingInformative Missingness of the OutcomeFDA Meta-Analysis   Part III:  Randomized Trials 14. Community Randomized Trials for Small Samples Introduction of SEARCH Community Randomized TrialAdaptive Pair MatchingData-Adaptive Selection of Covariates for Small SamplesTMLE Using Super Learning for Small SamplesInference 15. Sample Average Treatment Effect in a CRT Introduction of the ParameterEffect for the Observed CommunitiesInference 16. Application to Clinical Trial Survival Data Introduction of the Survival ParameterCensoringTreatment-Specific Survival Function 17. Application to Pandora Music Data Effect of Pandora Streaming on Music SalesApplication of TMLE 18. Causal Effect Transported Across Sites Intent-to-Treat ATEComplier ATEIncomplete DataMoving to Opportunity Trial   Part IV:  Observational Longitudinal Data 19. Super Learning in the ICU ICU Prediction ProblemSuper Learning Algorithm Defining Stochastic InterventionsDependence on True Treatment MechanismsContinuous ExposureAir Pollution Data Example 21. Stochastic Multiple-Time-Point Interventions on Monitoring and Treatment Defining Stochastic Interventions for Multiple-Time PointsIntroduction of Monitoring ProblemNon-direct Effect Assumption of MonitoringDynamic TreatmentDiabetes Data Example 22. Collaborative LTMLE Collaborative LTMLE FrameworkBreastfeeding Data Example   Part V:  Optimal Dynamic Regimes 23. Targeted Adaptive Designs Learning the Optimal Dynamic Treatment Group-Sequential Adaptive DesignsMultiple Bandit ProblemTreatment Allocation Learning from Past DataMean Outcome Under the Optimal TreatmentMartingale TheoryInference 24. Targeted Learning of the Optimal Dynamic Treatment Super Learning for Discovering the Optimal Dynamic ruleDifferent Loss FunctionsTMLE for the Counterfactual MeanStatistical Inference for  the Mean Outcome Under the Optimal Rule 25. Optimal Dynamic Treatments Under Resource ConstraintsConstrained Optimal Dynamic TreatmentSuper Learning of the Constrained Optimal Dynamic RegimeTMLE of the Counterfactual Mean Under the Constrained Optimal Dynamic Regime   Part VI:  Computing 26. ltmle() for R Introduction to the ltmle() R PackageDemonstration of the ltmle() R Package 27. Scaled Super Learner for R Introduction to the H2O EnvironmentR PackageSubsemble 28. Scaling CTMLE for Julia Scaling Computing of CTMLE in JuliaPharmacoepidemiology Example   Part VII:  Special Topics 29. Data-Adaptive Target Parameters Definition of ParameterExamples of Data-Adaptive Target Parameters as Arise in Data MiningEstimators of the Data-Adaptive Target Parameters Using Sample SplittingEstimators of the Data-Adaptive Target Parameters Without Sample SplittingCross-Validated TMLE of the Data-Adaptive Target Parameters 30. Double Robust Inference for LTMLE The Challenge of Double Robust Inference for Double Robust Estimators 31. Higher-Order TMLEHigher-Order Pathwise Differentiable Target ParametersHigher-Order TMLEKth Order RemainderParameters Not Second-Order Pathwise DifferentiableSecond-Order U StatisticsApproximate Second-Order Influence FunctionApproximate 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