Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Causal Inference for Machine Learning Engineers: A Practical Guide

Causal Inference for Machine Learning Engineers: A Practical Guide

Rajamanickam, Durai

Paperback

General ComputersProbability & Statistics

PREORDER - Expected ship date January 1, 2026

ISBN10: 3031996798
ISBN13: 9783031996795
Publisher: Springer
Published: Jan 1 2026
Pages: 245
Language: English
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.

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