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. 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 Biostatistics and the Journal of Causal Inference and associate editor of a variety of journals. 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 or 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 has made major contributions to the development and application of targeted 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 American Statistical Associationand Biostatistics.

Provides essential data analysis tools for answering complex big data questions based on real world data

Contains machine learning estimators that provide inference within data science

Offers applications that demonstrate 1) the translation of the real world application into a statistical estimation problem and 2) the targeted statistical learning methodology to answer scientific questions of interest based on real data

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