Synopsis
This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models--primarily focused on pattern recognition--often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson's Paradox, and will understand why these challenges necessitate a causal approach.
Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Durai Rajamanickam is a distinguished AI and data science leader with over two decades of experience, specializing in the application of machine learning to critical real-world challenges in healthcare, finance, and legal technology. Renowned for his ability to distill complex theoretical concepts into actionable solutions, he has spearheaded transformative AI initiatives across various industries.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Causal Inference for Machine Learning Engineers: A Practical Guide
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Causal Inference for Machine Learning Engineers (Paperback)
Edité par
Springer International Publishing AG, Cham, 2026
ISBN 10 : 3031996798
ISBN 13 : 9783031996795
Neuf
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Paperback. Etat : new. Paperback. This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning modelsprimarily focused on pattern recognitionoften fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpsons Paradox, and will understand why these challenges necessitate a causal approach. Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783031996795
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Causal Inference for Machine Learning Engineers
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Springer, Berlin, Springer, 2026
ISBN 10 : 3031996798
ISBN 13 : 9783031996795
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models primarily focused on pattern recognition often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson s Paradox, and will understand why these challenges necessitate a causal approach.Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. 245 pp. Englisch. N° de réf. du vendeur 9783031996795
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Causal Inference for Machine Learning Engineers: A Practical Guide
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Causal Inference for Machine Learning Engineers: A Practical Guide
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Causal Inference for Machine Learning Engineers: A Practical Guide
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Causal Inference for Machine Learning Engineers
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Springer Verlag GmbH, 2026
ISBN 10 : 3031996798
ISBN 13 : 9783031996795
Neuf
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Causal Inference for Machine Learning Engineers (Paperback)
Edité par
Springer International Publishing AG, Cham, 2026
ISBN 10 : 3031996798
ISBN 13 : 9783031996795
Neuf
Paperback
Vendeur : CitiRetail, Stevenage, Royaume-Uni
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Paperback. Etat : new. Paperback. This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning modelsprimarily focused on pattern recognitionoften fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpsons Paradox, and will understand why these challenges necessitate a causal approach. Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783031996795
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Causal Inference for Machine Learning Engineers
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Springer, Springer Jan 2026, 2026
ISBN 10 : 3031996798
ISBN 13 : 9783031996795
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -.- Introduction to Causal Thinking.- Treatments, Outcomes, and Confounding: Core Concepts.- Causal Estimation Basics.- Causal Graphs: Structure and Assumptions.- Interventions and Counterfactuals.- Introduction to Do-Calculus.- Backdoor and Frontdoor Criteria.- Advanced Causal Inference Methods.- Causal Inference Meets Deep Learning.- Simulating Causal Data and Evaluation Met rics.- Balancing Representations with Causal Deep Learning (CFRNet).- Propensity Scores in Causal Deep Learning.- Evaluating Causal Models Without Counter factuals.- Advanced Topics in Causal Inference.- Assumptions and Real-World Challenges in Causal Inference.- Summary of Key Concepts.- Case Studies.- Solutions to Exercises.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 268 pp. Englisch. N° de réf. du vendeur 9783031996795
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Causal Inference for Machine Learning Engineers : A Practical Guide
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive exploration of causal inference, specifically tailored for machine learning practitioners. It begins by establishing the fundamental distinction between correlation and causation, emphasizing why traditional machine learning models primarily focused on pattern recognition often fall short in scenarios that require an understanding of cause and effect. The book introduces core causal concepts, such as interventions and counterfactuals, and explains how these ideas are formalized through tools like causal graphs (Directed Acyclic Graphs, or DAGs) and the do-operator. Readers will learn to identify common pitfalls in observational data, including confounding, selection bias, and Simpson s Paradox, and will understand why these challenges necessitate a causal approach.Causal Inference for Machine Learning Engineers: A Practical Guide then moves to practical methods for causal estimation, detailing techniques such as regression adjustment, propensity score methods (including matching, stratification, and inverse probability weighting), and instrumental variables. The book delves into advanced topics such as mediation analysis, causal discovery algorithms (PC and FCI), and transportability, providing a roadmap for applying causal reasoning in diverse real-world applications across healthcare, economics, and the social sciences. A significant portion is dedicated to integrating causal inference with deep learning, introducing architectures such as TARNet, CFRNet, and DragonNet, as well as frameworks like Double Machine Learning, all designed to address the challenges of high-dimensional data and improve causal effect estimation in complex settings. N° de réf. du vendeur 9783031996795
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