🔍 The Definitive Guide to Causal Thinking in Science, Data, and AI

What causes what — and how can we prove it?

In Causality, Turing Award winner Judea Pearl introduces a revolutionary framework for understanding how cause-and-effect relationships can be identified, modeled, and tested. Whether you’re working in statistics, AI, epidemiology, economics, or social sciences, this book transforms how you approach reasoning and data.

Key Concepts Covered:

Structural causal models and counterfactuals

Graphical models and do-calculus

The limitations of traditional statistical inference

Practical case studies in medicine, machine learning, and policy

Tools for building truly intelligent AI systems

Why Choose This PDF Edition:

📄 Clear, searchable digital format — ideal for research and study

🧩 Includes diagrams, equations, and step-by-step reasoning

🏛️ Used in top-tier universities and data science programs

📚 Perfect for both self-study and academic reference

✅ Ideal For:

Data scientists and AI engineers

Researchers in social sciences, medicine, and policy

Statisticians and philosophers of science

Graduate students and advanced professionals

Challenge assumptions. Learn to reason beyond correlations.

Get your full PDF of Causality: Models, Reasoning, and Inference now on BetterBookly.com — where deep learning meets deeper thinking.

Preview: Causality – The Science of Cause and Effect in a Data-Driven World