How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.

In Causal AI you will learn how to:

• Build causal reinforcement learning algorithms
• Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro
• Compare and contrast statistical and econometric methods for causal inference
• Set up algorithms for attribution, credit assignment, and explanation
• Convert domain expertise into explainable causal models

Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes. 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 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.

Book Information
ISBN 9781633439917
Author Robert Ness
Format Hardback
Page Count 500
Imprint Manning Publications
Publisher Manning Publications