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

generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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Targeted Learning

applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible;

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Big Data in Omics and Imaging, Two Volume Set

theory to genomic, epigenomic and imaging data analysis Develop novel statistics for genome-wide causation studies and;

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Elements of Causal Inference

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The;

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Counterfactuals and Causal Inference

matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal;

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Counterfactuals & Causal Inference

matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal;

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Missing Data in Longitudinal Studies

Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for;

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Causation in Population Health Informatics and Data Science

thinking on causal inference in population health informatics. It is therefore a critical resource for all informaticians and epidemiologists;

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The SAGE Handbook of Regression Analysis and Causal Inference

longitudinal data with an emphasis on causal analysis and provides readers with an introduction to and exploration of a large range of techniques.;

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Epidemiology by Design

methods and thinking in causal inference. This new textbook will serve as a foundation not just for further study of the field, but as a head;

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Analysis of Longitudinal Data with Examples

Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which;

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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Agent-based Models and Causal Inference

evidence for specific causal claims provided by other methods. Manzo has done a most convincing job of showing that ABMs can be an important;

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Longitudinal Data Analysis

available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues;

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Confounding in Causal Inference

Causal inference is an important but controversial topic in the social sciences in that it is difficult to statistically control for all;

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Data Science for Public Policy

applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live;

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Data Science for Public Policy

applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live;

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Longitudinal Research with Latent Variables

number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only;

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Longitudinal Research with Latent Variables

number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only;

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Basic Principles And Practical Applications In Epidemiological Research

simplifies the design and inferences of human observational studies into two types: descriptive and causal. It clarifies how and why causal inference;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference;

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Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference;

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Designing Social Inquiry

framing research questions, measuring the accuracy of data and the uncertainty of empirical inferences, discovering causal effects, and getting;

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