How do you know what might have happened, had you done things differently? I this book, Robert Osazuwa Ness gives insights on how to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions. Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers, and provides a practical introduction to building AI models that can reason about causality.

<ASIN:1633439917 >

Author: Robert Osazuwa Ness
Publisher: Manning
Date: March 2025
Pages: 520
ISBN: 978-1633439917
Print: 1633439917
Kindle: B0DWNL5LG2
Audience: AI developers
Level: Intermediate
Category: Artificial Intelligence

Topics include:

• 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

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