Causal Inference for Machine Learning Engineers

Lingua: inglese

Editore: Springer, Berlin, Springer, 2026

3031996798 / 9783031996795

Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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Venditore AbeBooks dal 11 gennaio 2012

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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.

Codice articolo 9783031996795

Titolo
Causal Inference for Machine Learning Engineers
Autore
Durai Rajamanickam
Editore
Springer, Berlin, Springer
Anno di pubblicazione
2026
Condizione
Neu
Rilegatura
Taschenbuch
Lingua
inglese
ISBN 10
3031996798
ISBN 13
9783031996795
Dimensioni
235x155x35 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 11 gennaio 2012

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 5 a 15 giorni lavorativiDa 5 a 15 giorni lavorativi
Primo articoloEUR 23,00EUR 23,00
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BuchWeltWeit Ludwig Meier e.K.

Germania