Causal AI: Beyond Correlation by Ajit Singh
English | September 11, 2025 | ISBN: N/A | ASIN: B0FQYNDGL1 | 292 pages | EPUB | 1.01 Mb
"Causal AI: Beyond Correlation" is a comprehensive, practical, and accessible guide to the principles and practices of Causal Inference and its application in modern Artificial Intelligence. Designed for B.Tech and M.Tech students in Computer Science, Data Science, and related engineering disciplines, this book serves as both a foundational textbook and a hands-on manual for building more intelligent, robust, and interpretable AI systems.
Key Features of This Book:
1. Beginner to Advanced Trajectory: The book follows a logical progression, starting with the fundamental concepts of causality and gradually building up to advanced topics like Causal Machine Learning, Counterfactuals, and Deep Learning integrations.
2. Practical, Hands-On Approach: Every chapter includes hands-on labs and coding exercises in Python, using popular libraries like DoWhy and EconML. Readers don't just learn theory; they apply it.
3. Real-World Case Studies: The book is rich with case studies from various domains, such as evaluating marketing campaign effectiveness, assessing the impact of a new medical treatment, and building fair and unbiased algorithms.
4. Complete Capstone Project: The final chapter guides the reader step-by-step through a live, end-to-end Causal AI project, including data preprocessing, model building, causal analysis, and interpretation of results, complete with fully explained code.
5. Clarity and Simplicity: Complex mathematical ideas are broken down into simple, intuitive explanations, often supported by visual aids and analogies, making the subject accessible to a broad audience.
6. Focus on a Foundational Skill: This book teaches a timeless and tool-agnostic skill-causal reasoning. This skill will remain valuable regardless of how AI frameworks and technologies evolve.
For B.Tech and M.Tech students, who will be the architects of tomorrow's technological landscape, a deep understanding of causality is no longer optional-it is essential. Whether you are building economic models, designing clinical trials, optimizing supply chains, or creating fair and unbiased algorithms, the principles in this book will provide you with a powerful and indispensable toolkit.
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