Causal Inference in AI
Understanding Why AI Makes Decisions and How to Build More Reliable Systems
Move beyond prediction to true understanding of why your AI systems make the decisions they do. This book bridges the gap between traditional machine learning and causal inference, showing you how to build AI models that don't just predict accurately—they explain their reasoning and generalize reliably to new situations. You'll learn practical frameworks for identifying causal relationships in data, designing experiments that establish true causality rather than mere correlation, and integrating causal techniques into your machine learning workflows. Whether you're working with Midjourney, building recommendation systems, or developing predictive models, understanding causality transforms your ability to debug failures, improve robustness, and create AI systems that stakeholders can trust. This book combines rigorous causal theory with hands-on examples, showing you how to apply these concepts immediately to real-world challenges in AI development and deployment.
Est. Length
207 pages (9 chapters)
Language
English
Publication date
Nov 24, 2025
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