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Causal Inference for Machine Learning Engineers

A Practical Guide

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Springer


Paru le : 2026-01-01



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Description

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.
Pages
245 pages
Collection
n.c
Parution
2026-01-01
Marque
Springer
EAN papier
9783031996795
EAN PDF
9783031996801
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
24
Taille du fichier
4997 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
58,01 €
EAN EPUB
9783031996801
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
24
Taille du fichier
43919 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
58,01 €

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.

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