Targeted learning in data science. Causal inference for complex longitudinal studies
type: Item
instance of: scholarly article
title: Targeted learning in data science. Causal inference for complex longitudinal studies
zbMATH Open document ID: 1408.62005
DOI: 10.1007/978-3-319-65304-4
author: Sherri Rose
author: Mark J. Van der Laan
published in: Springer Series in Statistics
publication date: 2018-06-19T00:00:00Z
review text: The book consists of eight parts structured as a thorough overview of different aspects of targeted learning. The first part is built as an introduction to targeted learning in data science and comprises of four chapters leading to the definition of LTMLE. In the first chapter, the authors overview the main research questions in data science covering both the traditional approaches and newer methods. Next, the statistical estimation problem is presented in detail; the chapter concludes with a roadmap/pipeline for approaching targeted learning. In the second chapter, the authors describe methods for defining the structural causal model. The third chapter overviews methods for sequential super-learning. Starting with a description of ensemble learning, the authors continue with a definition of the estimation problem and remarks on computation. In the fourth chapter, the LTMLE is introduced using HIV treatment example. The different steps such as the definition of the estimation problem, the identification of the efficient influence curve, of the loss function and fluctuation submodel, as well as the analysis of the asymptotic efficiency are presented in detail. In the second part, the authors explore additional core topics including the one step TMLE (in Chapter 5, with a focus on the least favourable submodel), the highly adaptive Lasso (HAL, in Chapter 6, with characteristics introduced mainly through applications such as the prediction simulation and data analysis and simulation for missing data), a combination of two i.e. generally efficient HAL-TMLE (in Chapter 7), the HAL estimator of the efficient influence curve (in Chapter 8, with the details on the rate of convergence and the effect of using a subset from the truth data distribution), an approach for inferring data-adaptive target parameters (in Chapter 9, with details on the CV-TMLE and specific software for data-adaptive VIMs, varipact) and C-TMLE for continuous tuning (in Chapter 10, concentrated on the t…
reviewed by: Irina Ioana Mohorianu
Mathematics Subject Classification ID: 62-01
Mathematics Subject Classification ID: 68-01
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H11
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62J07
zbMATH DE Number: 6891537
zbMATH Keywords: data science
zbMATH Keywords: big data
zbMATH Keywords: targeted learning
zbMATH Keywords: statistical estimation
zbMATH Keywords: structural causal model
zbMATH Keywords: sequential super-learning
zbMATH Keywords: ensemble learning
zbMATH Keywords: longitudinal targeted maximum likelihood estimator (LTMLE)
zbMATH Keywords: targeted maximum likelihood estimation (TMLE)
zbMATH Keywords: canonical one-dimensional submodel for a multidimensional target parameter
zbMATH Keywords: highly adaptive Lasso (HAL)
zbMATH Keywords: HAL-TMLE
zbMATH Keywords: efficient influence curve
zbMATH Keywords: data adaptive target parameters
zbMATH Keywords: C-TMLE (continuous tuning)
zbMATH Keywords: targeted estimation of cumulative effects
zbMATH Keywords: sample average treatment effect
describes a project that uses: simcausal
describes a project that uses: gam
describes a project that uses: SuperLearner
describes a project that uses: histogram
describes a project that uses: XGBoost
describes a project that uses: arm
describes a project that uses: ltmle
describes a project that uses: tmle.npvi
describes a project that uses: tsml.cara.rct
describes a project that uses: nbpMatching
describes a project that uses: ipred
describes a project that uses: Pegasos
describes a project that uses: tmle
describes a project that uses: epitools
describes a project that uses: R
describes a project that uses: glmnet
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-319-65304-4
OpenAlex ID: W4240499199
description: scientific article; zbMATH DE number 6891537