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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Paperback, Softcover Repri)

Mark J. Van Der Laan, Sherri Rose (지은이)
Springer Nature Switzerland AG
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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies
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· 제목 : Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Paperback, Softcover Repri) 
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9783030097363
· 쪽수 : 640쪽
· 출판일 : 2018-12-15

목차

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

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