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about the technology

Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars.

about the book

Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You’ll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you.

about the reader

For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python.

about the author

Robert Ness is a leading researcher in causal AI at Microsoft Research. He is a contributor to open-source causal inference packages such as Python’s DoWhy and R’s bnlearn.