Causal AI Beyond Correlation

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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